<issue_start>username_0: While reviewing research papers, I often find that the author's English is not very good. Considering that English is not everyone's native language, I understand their difficulties, and I recognize that they struggle (using dictionaries and translators) to get their work published at all.

That being said, I sometimes find papers that have contain English that is frankly terrible, with a few select fragments in pristine, almost Shakespearean English, using words that sometimes I didn't even know existed. When this happens, a quick Google search will occasionally reveal that these fragments are copy-pasted fragments from textbooks.

What should I do in these cases? I feel that being too harsh might come of as being mean, but I would really like to emphasize that the practice is very bad for the scientific community.<issue_comment>username_1: This is plagiarism. You should at the VERY least point this out to the editor and suggest (s)he insist the authors get language, translation or copyediting assistance if they can't write their paper themselves.
Upvotes: 4 <issue_comment>username_2: As Mikael said, copying and pasting from a textbook (or any other source), unless it's properly quoted and cited, is plagiarism. Now, it's possible that this varies a bit between fields and between cultures, but generally speaking, **plagiarism is one of the most serious academic offenses**, probably a step below outright fabrication of results. Well-respected tenured professors at major universities have lost their jobs and had their careers destroyed by a single instance of plagiarism. The point is, this is a very big no-no and you should treat it accordingly. You should definitely notify the editor, and see how they would like you to deal with it. I don't know exactly how that process works, whether the editor will just handle the paper themselves and let you know that your review will no longer be necessary, or whether they'll ask you to finish your review anyway and state your objections in it.

In the latter case, I would write an unequivocal recommendation *against* publishing the paper because of the plagiarism. Personally, I would be inclined not to even look at the scientific content of any such paper, although that may not work out in practice. But anyway, just pointing out the plagiarized parts and recommending against publication, in and of itself, is not mean. Just don't get carried away and start attacking the author. You could apply the same principle that is used at Stack Exchange, namely that it's about the behavior, not the person.

---

If you put aside the plagiarism, there is also the issue of sloppy English that you mentioned. In my experience, (nearly?) all reputable journals require papers to use proper spelling and grammar for standard English, or something reasonably close to it. Usually, the instructions for authors will advise non-native speakers of English to get a native speaker to check the paper for grammatical errors before submission. So it's reasonable for you to point out any such errors that you come across. Now, grammatical errors don't have to condemn a paper to oblivion the way plagiarism might, but if the grammar is bad enough, I would think it reasonable to recommend against publication in its current state. If the underlying ideas are sound, and otherwise qualified for publication, then you could recommend that the authors edit the paper to improve the grammar and resubmit it.
Upvotes: 6 [selected_answer]<issue_comment>username_3: I understand exactly what you are going through. I have seen it happen many times before. In some culture it is extremely common for students to think that it is acceptable to plagiarize in this way. However, in my experience, even when students think it is acceptable, they still know it is wrong.

You should call it out for what it is: Plagiarism. In my experience, some students only wake up to the impact of plagiarism when they are shocked into it. I've had students call me mean (and much, much worse) but I've never had a student say that I was unjustified in calling their plagiarism out. They knew what they were doing and they got caught.

I also explained to them how to "right their ship" so this did not cause future problems. In my school, the punishment is quite lenient for plagiarism - just fail (no removal, no academic probation, etc.). But, it is still expected that it is called out when it is found.
Upvotes: 3 <issue_comment>username_4: In the couple of cases that I have found plagiarism (and that is what has happened here). I always contact the editor directly telling him/her what I have found and that I no longer feel comfortable writing a review. This then puts the burden on the editor to make a decision of how to deal with it.
Upvotes: 4 <issue_comment>username_5: I would see two situations here:

* The English is so bad that you cannot properly review the paper. If you suspect that there is merit in the paper, suggest getting the language polished and encourage to resubmit. Otherwise reject the paper.
* You can follow the story, and provide an in depth review. Just reply as you would normally, and mention polishing the language as one of the additional remarks.

Just copying without reference is just plagiarism, and should lead to rejection of the paper.
Upvotes: 3


<issue_start>username_0: I was reading about a “young researcher award” recently being attributed in my field to a colleague who is 40 years old (and has had a permanent research position for 12 years now). It may be viewed as borderline ridiculous, so I must ask: in this context, how young is typically “young”? For the purpose of this special projects/grants/awards, how do research and funding organizations define “young”?<issue_comment>username_1: The EU has the program [FET OPEN Young Explorers](http://cordis.europa.eu/fp7/ict/fet-open/ye_en.html), where young is defined as:

>
> A project must be led by a young researcher, and the leadership by young researchers of all work packages is also required. No more than six years should have elapsed between the award of a Ph.D. (or equivalent) for each such young researcher and the date of submission of the short proposal.
>
>
>

I have also seen postdoc position reserved for people who obtained their PhD less than 5 years ago, but I can't find the reference back.
Upvotes: 3 <issue_comment>username_2: "Young" does in fact have many different connotations. For example, in the [ERC Starting Grant](http://erc.europa.eu/starting-grants) program, applicants can have been out of graduate school for as many as *twelve* years before applying as a "Starter" (or "Consolidator"). So technically, they're still "young," according to that program, but not what we would normally think of as "young."

But, in general, "young" can refer in terms of age (in which case I've normally seen the cutoff be about 35 or so), or in terms of relative experience in the field (typically five years, although it can be shorter or longer, depending on the field or country).
Upvotes: 4 <issue_comment>username_3: In economics, the most prestigious award outside the nobel prize is probably the John Bates Clark Medal (most winners go on to eventually receive the Nobel too). It is awarded biannually to only one person, so in a sense is even more prestigious than the Nobel (which is awarded annually to typically more than 1); to who is deemed the leading 'young' researcher under 40. So 40 as a cutoff for 'young' doesn't surprise me much, but there are obviously no hard rules.
Upvotes: 4 <issue_comment>username_4: The Fields medal also has an age cutoff of 40 (<NAME> being the most recent example of someone who missed the age cutoff for an otherwise sure award).

Grant agencies usually define "young" in terms of "years since Ph.D", or "years in academic position" with the typical cutoff being 6 or so.

Note that while for most things\*, 40 might not be considered "young", in many disciplines you might only get your first academic position near that age (especially in biology).

  \* politics being the other venue where 40 would be considered young :)
Upvotes: 4 <issue_comment>username_5: Here are some awards presented to people aged 35 and below:

1. The [Young Scientist Awards](http://www.a-star.edu.sg/tabid/443/default.aspx) of the Singapore National Academy of Science
2. The [Young Scientist of the Year Award](http://www.esrf.eu/UsersAndScience/users_org/YoungScientistAward) of the European Synchrotron Radiation Facility
3. The [Young Scientist Award](http://csirhrdg.res.in/ysa1.htm) of the Council of Scientific & Industrial Research
4. The [WMO Research Award for Young Scientists](http://www.wmo.int/pages/about/awards/youngscientistsaward.html) of the World Meteorological Organization
5. The [Young Scientist Award](http://epdic13.grenoble.cnrs.fr/spip.php?article18) of the European Powder Diffraction Conference

There are many more; just use a search engine to search for the terms *"young scientist" award 35*.

A similar list of awards can be found with a search for *"young scientist" award 40*.
Upvotes: 4 [selected_answer]<issue_comment>username_6: Sometimes what is still considered "young" also depends on the specific situation of the individual: I've seen exceptions described in the areas of paternity leaves, compulsory military service, or receiving additional clinical training.
Upvotes: 1 <issue_comment>username_7: The NSF [CAREER](http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503214) award (Faculty Early Career Development Program) is a grant for "young" scientists, and requires that winners have the title "assistant professor." This typically means that the person has spent < 6 years in a tenure track academic position. Of course, such a person need not be "young" -- someone who worked 2 years in industry before grad school, spent 6 years doing a PhD, 2 years of postdoc, and then spent 5 years as an assistant professor might win one of these awards and be nearly 40.
Upvotes: 0


<issue_start>username_0: How important is it for an undergraduate student majoring in Physics and Mathematics (or any other science) to have experience in research (*e.g.*, have a peer reviewed paper to his name most probably in collaboration with a professor) at the undergraduate level, keeping in mind that he will be applying for a master's or other postgraduate degree?<issue_comment>username_1: During my studies it was relatively rare to publish as an undergraduate. The level of research needed for a peer reviewed publication is imo higher than what an undergraduate can produce. Maybe if the supervisor writes a paper based on your results, and with a lot of help with producing the results, this might lead to a co-authorship for the undergraduate. Therefor, I think that in the Dutch system (my experience) a peer reviewed publication would be a plus, but definitely not a requirement for admission into a masters program. Ofcourse, you need have written a thesis, but it does not have to published in a peer-reviewed journal. I do not know how this experience translate to, say, the US, but I know for countries like Germany it is not even always usual for a PhD to write peer-reviewed articles.
Upvotes: 2 <issue_comment>username_2: If you want admission to the very best PhD programs in the US, prior formal research experience is *very* important, if not necessary. Admissions committees are primarily looking for **evidence of research potential**. The best possible evidence for "I'll be a good researcher someday" is "Look, I'm *already* a good researcher." So having formal peer-reviewed publications is better than having publishable but unpublished results, which is better than having research experience but no publishable results, which is better than having no research experience. If you're applying to the top PhD programs, you *will* be competing with applicants (yes, plural) who have peer-reviewed publications (yes, plural).
Upvotes: 5 [selected_answer]<issue_comment>username_3: In the U.S., in mathematics, it is a bit unusual to have a peer-reviewed publication from an undergrad, despite the recent years' push for "Research Experiences for Undergrads". In some cases there are group-written papers in second or third-tier journals, but nothing too serious. Or the undergrad gets to be the tag-along on an applied-math research "team". Indeed, it is exceptional, and only rarely happens, that an undergrad in mathematics has adequate background (disregarding future potential) to make a serious contribution. It does happen, but rarely, and is not at all "expected". Evidently the situation is much different in other fields.

In terms of literal admission to good-but-not-elite programs, the usual "publications" we on admissions committees see are "nice", but not really evidence of future potential so much as *enthusiasm*, ... which is a good thing, for sure! ... but the level of focus and effort required for these little papers is far, far different than the level of commitment required to do a Ph.D., with or without "talent".
Upvotes: 3


<issue_start>username_0: I could have written this question in some other stack places, but prefer to do here because the affinity with the audience. I am the head of a research group, 38 years old, engaged in a lot of things and working on average 12 hours per day on research and all related stuff. My son (first one) was born last week and we really enjoy it. My only concern is: how will I be able to cope with my workload and scientific output after my paternity leave (in two weeks)? I really never thought about this and wonder whether other academics could give advice.<issue_comment>username_1: I was close to vote to close for this question, however, in view of the upvotes, I'll give my two cents.

I think you are asking yourself the wrong question. You should not ask "How will I be able to keep up with my workload and scientific output?" but rather

>
> What I am willing to do for my work and for my family? How do I and the rest of my family should arrange our life including work hours, family time and all the rest?
>
>
>

You may come to the conclusion that a reduced scientific output may be a "good price to pay" for a richer family life. On the other hand, if you are pre-tenure for example, you may also reach the conclusion that a year or two of "not so rich family life" and more hours at work are worth it.
Upvotes: 4 <issue_comment>username_2: Can you *produce* as much, or even more, with less time available? Yes.

Is it easy? No.

What generic advice can be given? Little, especially in such short space. Numerous entire books have been written on the topic of time management. Read the advice of others, decide whether it can apply to you, try it out.

---

Now, the above isn't very helpful. Not in the short term, at least. Below are a few tips from my own experience, maybe you will find them helpful:

* First, **decide what your priorities are**, what you are willing to compromise, what you aren't. When I read *“engaged in a lot of things”*, it sounds like a warning to me. Honestly, the first two years of a baby's life are hard on the parents. Social life is hard to maintain, work output too, etc. Decide which you want to keep most, and focus there. **Don't set your expectations too high across the board.**
* Second, learn to say *no*. People at your department probably ask you to participate in all sorts of research-related work (committees, etc.). If you have been playing ball ’til then, probably they can understand if you say *“I'm sorry, right now is not a time I can take some extra work, I need to focus on my family”*. Don't use that as an excuse to get out of every commitment, or avoid anything that is thrown your way, but strategically get a bit more time that way. **Most people are quite understanding of the hardship of having a newborn baby**. (Or my colleagues and bosses are extraordinary people; knowing them, it's also quite possible!)
* **Delegate stuff**. You lead a team, so you have some responsibility in sharing the load. You can try to offload yourself a bit, by getting others to step in.
* **Work whenever you can (read: when the baby sleeps). Adjust your work style.** I'll give only one example: when the baby gets to cry real bad in evenings or at night, I noticed I could get her to sleep if I had her in a sling against my chest and rocked from one foot to the other:

![enter image description here](https://i.stack.imgur.com/K0KB5.jpg)

(the sling was as in the picture, except I needed to be standing and rocking). Well, I put my laptop on top of a high table (bar table), and quickly adapted to type and rock the baby. That way, I had time to work at night, while her mother could rest. I wrote quite a few grant proposals and articles that way.
Upvotes: 5


<issue_start>username_0: I'm a PhD student in physics, and I sometimes tutor undergrads in introductory physics classes on the side. Now, there's widespread agreement among people who teach or tutor physics that the only way to effectively learn the material is to *use* it, generally by working through practice problems in homework assignments and sample exams.

But recently I was contacted by a student who was interested in "tutoring," where by use of the quotes I mean that what she wanted was for *me* to do problems, and she would watch and ask questions when she was confused about something. At one point this student wrote a long email full of what seemed like psychological mumbo-jumbo trying to justify her assertion that she actually learns that way, and that the normal method of having *her* do the work would not be effective. I didn't believe it (thus I declined the tutoring assignment), but was I wrong? Are there actually students who don't learn by doing the work, and for whom it *is* a more effective strategy to just show them how to go through a problem? I would definitely appreciate pointers to educational psychology research on the matter, if anyone knows of any.

Note that I'm not talking about how to teach a topic for the first time, to a group of students who have never seen it before. In that case I know a bit of demonstration is necessary. The situation I'm asking about is reviewing for a final exam, where the student would (or should) have already learned the material in class and completed a homework assignment or two on it.<issue_comment>username_1: I am not an expert in cognitive science or psychology but I have a few years of teaching assistant experience behind me so I will have a go at this one and I suspect that a few others might even agree with me.

My general roles are in leading discussion sections and in lab sessions (information science: so combination of coding and data analysis in context with social science literature)

Now learning by watching aka [observational learning](https://en.wikipedia.org/wiki/Observational_learning) is not a [new](http://psuc0e.wordpress.com/2013/02/11/learning-by-watching/) concept. Scientists have been studying this for a long, long time. However, note the example literature cited in the previous links. There is a marked difference in learning how to strike a [Bobo doll](https://en.wikipedia.org/wiki/Bobo_doll_experiment) and acquiring a complex [skill](https://en.wikipedia.org/wiki/Skill) such as deriving [Newton Law's of Motion](http://www.ehow.com/how_7804623_derive-newtons-laws-motion.html)

Having laid all of this expository work, I would like to argue that personally, I **believe** that observational learning exists and happens in the natural world around us. I just don't believe that one can acquire complex mathematical notions and concepts just by observing (unless this girl is some sort of a savant).

I think that this student was just trying to make you do her homework problems for her.
Upvotes: 5 [selected_answer]<issue_comment>username_2: I have done almost all of my math learning so far this way. Consequently, I know a whole lot of math and I know a whole lot *about* a lot of areas of math, but I suck at proving things, and my knowledge isn't terribly deep. Thankfully, I am finally going to be taking some pure math courses where I will have to prove things and develop my skills.

Less generally, I think that one can learn facts and knowledge and problem solving strategies by watching, but they will not develop skills. For that they need practice.
Upvotes: 2 <issue_comment>username_3: I think the student is reasonable, up to a point, then she is unreasonable.

When tutoring a student (teaching of any kind, really), you should give some examples in order to give her the key information she will need in order to do the work which will be expected of her. However, after you have covered the key points, which includes showing her how to work through a problem or two (or three, depending on the situation) then you must assess her learning (and your teaching).

The natural way to assess is to have her produce, for example by solving problems. If she only wants to watch you and does not want to produce then it seems she is not interested in you assessing her and she is not interested in assessing herself. I would not start (or continue) tutoring this kind of student.

So, yes, perhaps she does learn best that way...but part of the educator's job is to see if the education is actually taking hold and the only way to do that is to have the student produce something.
Upvotes: 3 <issue_comment>username_4: If one were to wager, the smart money is that it's a con. However, in principle, an exceptional person certainly does not need to be subjected to the orthodox fake-rigor fake-serious pretense. That is, a person with some natural talent catches on very, very, very quickly, while other kids are effectively resisting and thinking of something else anyway.

By this point in my life, I am inclined to give people a chance to "be talented", rather than to doubt that they are, even while I might privately bet against their self-estimate or pretense. After all, letting a few people "game" the system while we treat serious people well is better than brutally policing everything, crushing sincere talented people, just to be sure that no one "gets more credit than they deserve".

Btw, I think it's not about "learning styles", but about degree of engagement, optimistically. (Or else it is a con, but it's better for all of us to try to believe it's not...)
Upvotes: 0 <issue_comment>username_5: I would like to provide another perspective to this question; regardless whether or not the student is trying to fool you into doing her work for her, you can take a look at it from a practical perspective.

You (=tutor) are required to teach to her (=stuent) and presumably to many others like her. Likewise you are expected to read articles and do research. I would even argue that your primary task is to do research, not to teach undergrads. So, while your responsibilities are to prepare material and give lectures etc, their responsibility is to go through the material and do the work (hw/assignments). Given that you provide enough examples during the lectures, I don't see why you should go through the review or homework assignments for the student(s).

After all it's probably not feasible (time) or reasonable for you to do all that. Are you going to have individual sessions for every single student, so that their thoughts don't get disturbed by others who might ask questions? A compromise could be to hold *one* lecture where you solve old exam questions for the whole class. Our calculus teachers used to do that. THey would take an old exam, and solve each question, explaining as they go.

I am all for helping people with special needs (hearing aids, dyslexia etc) but *"I don't want to do the homework assignments, you do them for me and I watch you solve them"* is not a special need, it's good old laziness.
Upvotes: 0


<issue_start>username_0: Assume that I publish many papers in many journals and someday I changed the family name (my last name). How can I edit that where my papers have been published? and is it easy process?<issue_comment>username_1: I am afraid that you can't easily change the name on a published article. Publishing is (at least theoretically) still done on print, so there is simply no way to change the physical journal once it has been sent to university libraries all over the world. Even for more important issues such as plagiarism or factually incorrect data, only an errata is issued, or the paper is marked as "retracted".

What you can do instead is advertising the double name on your webpage and CV, and ensuring that the academic databases (such as Web of Science and Scopus) correctly recognize and handle your name change, marking all of your papers as written by a single author. You will probably need to notify them using the "contact us" functions on their websites.

Several authors in the same situation choose to keep the old name also on new papers; this makes it simpler for other researchers to recognize you, at the price of using a name that you might have disowned and now consider a relic from the past. In practice, there is no requirement that your academical *nom de plume* coincides with the one that is written on your ID and that you use on legal papers, so you are free to sign your papers using a different version of it. Once you choose this route, however, it will be more practical if you consistently use the old name also when attending conferences.

If you are simply getting married, then signing your papers with both surnames is probably the easiest option. (I realize that probably you have already considered and discarded this option, but I thought it more appropriate to include it in my answer anyway.)
Upvotes: 5 <issue_comment>username_2: As others have pointed out, there is almost certainly no way to change your name in already published papers. In line with the previous answer, I would like to call your attention to initiatives like [ORCID](http://orcid.org/) and [ResearcherID](http://www.researcherid.com/Home.action?returnCode=ROUTER.Unauthorized&SrcApp=CR&Init=Yes) that aim at creating unique identifications for each researcher, so you can collect all your scientific output under a single ID, more or less independently of a particular name or spelling.
Upvotes: 4 <issue_comment>username_3: There are two strategies that I have heard of that people with renowned accomplishments tend to employ when they get married (what is, I think, the most common situation when a person changes last name). It certainly depends on your legal system, but in my country they either: stay by the old name or use composite last name. The latter meaning that if a person's name was Smith and nupturient's name is Brown, then they change the name to Smith-Brown.
Upvotes: 0


<issue_start>username_0: I just finished my master and am about to start a funded PhD in October. Thing is that the reason I am doing this PhD is because I missed the deadlines for the PhDs that I am truly interested in. I was thinking that it would be better to do something academic this year and get paid than to do nothing until the next application cycle.

* Is this extremely dishonest to start a PhD without the intention to finish it? Will this put me on some blacklist at that university?
* How should I portray my position in my other PhD applications? I was thinking of saying that I was doing a funded student placement program or something?<issue_comment>username_1: >
> Is this extremely dishonest to start a phd without the intention to finish it?
>
>
>

It depends if you talk about your situation with your current advisor. If you clearly stated *"I'm more interested in another position, but I couldn't get it, and it's likely I'll try next year"*, then, no, it's not dishonest. If you said you were interested in doing a full PhD there, while you actually were not, then, yes, it's dishonest. You're taking a funding opportunity for someone who would be interested, and in some cases, for instance when the funding is associated with a project, you will leave your current advisor with a partial funding, that cannot be used to hire a new PhD student.

>
> Will this put me on some blacklist at that university?
>
>
>

Again, it depends how you explained the situation, but I would not hope much for any recommendation letter from your current advisor (which is a shame, because recommendation letters can be *very* useful later on).

>
> How should I portray my position in my other phd applications? I was thinking of saying that I was doing a funded student placement program or something?
>
>
>

As I said [here](https://academia.stackexchange.com/a/5447/102), lying is always a bad idea. Academia is a very small world. If you want to change position, then you have to explain why.
Upvotes: 5 <issue_comment>username_2: For some of us, being able to research as a paid job is one of the best that could happen, for some others its *just another job*. No matter what advise you will get from here, your decision will depends on the way you look at it. From the spirit of your question, I think that you will fall into the later with an *extra intention* that you would be able to contribute to academia.

>
> Is this extremely dishonest to start a PhD without the intention to finish it? Will this put me on some blacklist at that university?
>
>
>

That will depend on the nature of project and the type of contract you would be signing. For me, the contract is for 3 years and the project was designed such that there would be experiments and analysis during these years. As you would expect, the first few months would be learning the necessary tools and methods. If I chose to quit, that will put the whole project in jeopardy and if the group decides to take another candidate for the same project, unless they couldn't find someone who already know the tools to work, it is a huge waste of productive time.

>
> How should I portray my position in my other PhD applications? I was thinking of saying that I was doing a funded student placement program or something?
>
>
>

Well, I suggest you tell the truth. Everything else would be trying to suppress what you had been doing. You really don't like the whole idea, do you?

You will never know if the person who would have been accepted instead could contribute more than you will in your proposed year, is passionate, or just another candidate looking for just another job. I am sure that you got the position because you are eligible for it, and you are in full liberty to choose what you do with it, but as I mentioned the first part, an ethical dilemma occurs when your intentions could jeopardize the whole project.
Upvotes: 0


<issue_start>username_0: I just graduated from one of the older IITs (Indian Institute of Technology), majoring in EE and have a GPA of about 7.8 / 10. I want to pursue a masters degree in computer science from a top 20 university in the US. I have a paper published in an international conference on machine learning and have good test scores(GRE / TOEFL) but I'm worried that my grades could affect my chances. I have some experience in competitive programming, my team finished among the top 10 among 300+ teams in the ACM ICPC regionals and I have a decent Topcoder rating. Not sure if this matters though. I have also made contributions to open source projects and have open sourced some of my projects, some of which have been downloaded thousands of times.

I've seen people from unknown colleges with higher GPAs getting in to places like Stanford, UIUC, Cornell, CMU etc. Realistically, do I have any chance of getting admitted in a top 10 or 20 program. If not, what can I do to improve my chances within a year?

UPDATE: Well, I guess grades do matter, I didn't get into any of the good schools I applied to. I'll have to give up my hopes of grad school now.<issue_comment>username_1: While your question is not a duplicate of [Maximizing opportunities to be admitted in top schools](https://academia.stackexchange.com/questions/1034/maximizing-opportunities-to-be-admitted-in-top-schools) or [How do you get a bad transcript past Ph.D. admissions?](https://academia.stackexchange.com/questions/324/how-do-you-get-a-bad-transcript-past-ph-d-admissions/325#325), since in general MS admissions is different from PhD admissions, they may be helpful.

As I said in another [answer](https://academia.stackexchange.com/a/1900/929), your approach is wrong and will be a hindrance in maximizing your chances for acceptance. No school wants to accept someone who only wants to go to their school because of its ranking. You need to tailor your application to demonstrate why you want to go to the particular school.

If the MS programs you are interested in have a research component, you need to identified some potential supervisors, ideally one of your recommendation writers can introduce you. You then need to build on that relationship. Talk to them about how your work fits in with their past and current work. As your relationship with the potential supervisors builds, figure out how they fit into the department. They will likely have the inside knowledge needed for writing a really good application.

For purely taught programs you need to identify what it is about the courses they offer that make the program unique and a good fit for you.
Upvotes: 2 <issue_comment>username_2: >
> Realistically, do I have any chance of getting admitted in a top 10 or 20 program.
>
>
>

With a publication in an international conference? Definitely!

Of course, I'm assuming that you mean a **good** publication in an **internationally recognized** conference (not just a conference with papers from more than one country), ideally indexed in [DBLP](http://www.informatik.uni-trier.de/~ley/db/) and available over the web. I'm also assuming that you will have strong recommendation letters describing your contributions to the paper, in specific, personal, and credible detail, as evidence of your research potential. Ideally, your letters will make direct comparisons with other IIT graduates who have succeeded in strong graduate programs.

I'm also assuming that you have a compelling [statement](https://academia.stackexchange.com/a/1555/65) describing your research experience and interests, written in flawless English, and that those interests are a good fit for the faculty at your target departments. Be sure to describe your ongoing open-source projects. (The contest stuff? Meh.) As Daniel suggests, focus on the **work** you want to do, not the ranking of the department.

And of course, I do mean you have a **chance**, not that you have a sure thing. Graduate admissions is a random process; you have some influence on the distribution, but someone else rolls the dice. Machine learning is a particularly competitive area, so if you want to continue in that direction, you should apply broadly. In particular, you should *not* limit yourself only to programs that appear at the top of some list in some stupid magazine. Rather, aim yourself at departments whose faculty and students have similar research interests to your own.

(I regularly serve on the CS grad admissions committee at UIUC.)
Upvotes: 4 <issue_comment>username_3: You don't have a chance of getting into a top 20 university because you don't have enough GPA for that. Top 20 universities are for students getting GPA of above 9.5. But in your case, excluding the GPA you have a great chance of being accepted to top 40 universities like University of Virginia but Cornell, Stanford and all ... You can't just dream of that. So please try for the university which you can afford mentally, academically and financialy.
Upvotes: -1


<issue_start>username_0: I submitted to a journal a couple of months ago. The editorial flow is somehow quick and very transparent. I receive notifications for all events happening to the manuscript.

Unfortunately, the managing editor contacted me three weeks ago. They informed me that the assigned editor could not commit to the job and left. Another editor was immediately assigned.

However, I was contacted today by the new assigned editor. I was informed that all the contacted reviewers so far either refused to review the manuscript or did not respond to the invitation. They are now looking for other reviewers.

Despite the bad luck surrounding the manuscript, all of this happened in a reasonable time and I am satisfied with how quickly they react.

Nevertheless, these events made me reflect. Even if an editorial board possesses a fair network of reviewers, there is still a little risk that all the contacted reviewers either refuse or do not reply.

What happens if no reviewers could be found for a submitted manuscript? Is the journal going to reject the manuscript because of a lack of reviewers?<issue_comment>username_1: Short answer: the editor will continue to try and find other suitable reviewers, sending them invitations and waiting for replies. At some point, they may come back to you and ask you to suggest names (additional names, if you had already given some when you originally submitted the paper).

---

It can happen that an editor has a hard time finding suitable reviewers that accept to review a manuscript. There are a few factors in play, such as:

* the journal is not well-know;
* the editor is new, or not well-know in the field, or does not have a very good network;
* the research reported is atypical, in a very narrow subfield, or joins different areas (so that no one reviewer feels confident in accepting)
* the elements sent by the editor (title and abstract) are rather boring or tend to confuse the potential reviewers

Note that most of these factors do not reflect badly on the manuscript, so there is no need to feel bad about it. The editor is likely to continue his search of reviewers. I see two other options that the editor could choose from, but I judge them as rather unlikely:

1. Evaluate the manuscript himself, and make a decision based on his own review. After all, it's his job, and the reviewers' role is only to help the editor reach a decision.
2. Refer the manuscript to another editor, or to the editorial board, so they can make a decision on it.

Finally, note that it may take quite some time to find suitable (and willing) reviewers. In the case of one paper of mine, it took the editor 3 months to find adequate reviewers, and that was actually for a prestigious journal. However, the paper was atypical enough (and the research was quite novel) that many potential reviewers did not feel able to review it adequately. *(And in case you wonder, it was accepted on the first try, once the editor found reviewers.)*

---

Edit (regarding your comment): my advice is **don't retract your submission**, unless you think it's the journal's fault (unwilling editor or unknown journal). I know it's tempting! But especially if your work is multidisciplinary, it will take time to find reviewers, even if you submit it to another journal. Thus, better let the current submission process go to its end.
Upvotes: 5 [selected_answer]<issue_comment>username_2: F'x's answer looks right to me, but since this actually happened to me once, I'll add some extra commentary which doesn't sound like it applies to your situation, but might apply to similar ones.

Many journals (at least in math) have a de facto policy that every paper which even plausibly might get published has to go to a referee before a decision can be made. Therefore an article which has problems which make it very unlikely to get accepted---being difficult to read, or highly specialized, or involving multiple areas with few potential referees---but which doesn't have obvious flaws which make it an easy rejection might have trouble getting through the process. The editor keeps asking people to review it, and it looks like a hard job, and the paper isn't so interesting to be worth it, so they keep turning it down. An editor can eventually break this loop by asking a reviewer in a way which implies that the reviewer has permission to give a more cursory rejection for the reasons the editor has identified (perhaps based on comments made by other people when turning down the review).

Once an editor has decided to start nudging potential reviewers in this direction, it becomes faster for everyone if the editor politely suggests that the paper should just be withdrawn.

This (I think---I can't say for sure what the editor was thinking) is what happened to me. I submitted a paper involving two different areas to a journal specializing in one of them. After several months I got back a letter from the editor stating that several people had turned down the paper with negative impressions; the editor offered to look for more referees, but before doing so, wanted to know if I would like to withdraw the paper rather than continue to wait when it wasn't clear the paper would be accepted. I (and people I consulted with) interpreted this as a polite indication that at this point the editor would primarily be looking for an excuse to reject the paper, and therefore that it would speed things up for me if I withdrew it. (And, indeed, the next journal accepted it.)

So a notification that the editor can't find reviewers *could* be a hint that the editor wants you to withdraw it, but isn't necessarily.
Upvotes: 3 <issue_comment>username_3: It happened to me once that the editor sent my submitted paper to 5 reviewers (all of them were in my references) and all of them rejected the invitation to review the paper. The editor took this as a sign that my paper was not suitable for publication in that journal and rejected the paper. So, yes, a rejection can actually happen but it is not the rule. As usual, publshing is a combination of good content, a bit of politics, and luck.
Upvotes: 3 <issue_comment>username_4: In my experience, as long as one tries long enough, hard enough, one can always find reviewers. It's just a question of how long it takes. After all, performing literature reviews is standard Masters-level work, and it's a small step from that to finding researchers working in the area. In other words, if the first few sets of invited reviewers decline to review or do not respond, one of these things happen:

* The authors lose patience and withdraw the manuscript.
* The editor loses patience and rejects the paper due to "unable to find reviewers".
* Nothing, and the editor keeps trying to find reviewers.

It takes a motivated editor to keep trying, though. Even if the editor doesn't lose patience, it's possible he'll decide the paper can't be very good because otherwise reviewers would be jumping at the chance to review it.
Upvotes: 1 <issue_comment>username_5: Well, this happened to me recently. After 2.5 months of reviewer hunting, the editor-in-chief failed to find any reviewer and rejected my manuscript. The funny thing is that during this last 2.5 months, I inquired about the status of the manuscript thrice through the manuscript submission system and also emailed the editor-in-chief at his persona email. I never got back any reply from him. Finally, I emailed Elsevier complaining about lack of enthusiasm of the editor-in-chief and voila, after few days the paper was rejected!
Upvotes: 2


<issue_start>username_0: I am applying to an MS program in CS. I have not published any technical papers, mainly because I had little opportunity to do research and I did not want to write a paper just for the sake of it. Will I be rejected for this reason? Also, do I stand a less chance of admission than my friend who has written an average technical paper (dumb topic IMHO)?<issue_comment>username_1: There is no way to predict whether you will be accepted or rejected from any graduate program. Graduate admission is a random process. You have some influence on the expected outcome, but the variance is set differently by each department, and you only get one trial per department per year.

That said, as far as I know, there are no CS MS programs that restrict admission to applicants with prior publications. There **are** PhD programs where most students in some research areas had published (or at least publishable) results as undergraduates—algorithms at Princeton, for example. But even in those programs, and in those research areas, publication is not an actual requirement for admission.
Upvotes: 3 <issue_comment>username_2: It is a little bit of a simplification (and an avoidance of your actual question), but in general the only reason applicants get rejected from a graduate program is lack of space and funding. Being rejected generally doesn't say anything about you or your application. All you can take away from a rejection is that the department ran out of space and funding before they got to your excellent application.
Upvotes: 2


<issue_start>username_0: Patents are not normally considered as publications in scholarly journals, as they are not subject to peer-review. They are actually new ideas, but patenting does not guarantee the impact and importance (whether good or bad idea). However, academics tend to patent inventions and discoveries for the sake of possible commercial benefits.

I wonder if they have any considerable impact on academic career? e.g., promotion, getting academic jobs, and in a different context, impact on proposals for getting grants and research funds.<issue_comment>username_1: The answer will clearly vary by institution, and in particular by field (Your average English prof wouldn't be expected to hold any patents but a materials scientist might) but yes, it certainly can. Patents and publications are measures of productivity and it would be silly not to note them. It should be pointed out, however, that [patents correlate to jobs closer to industry](http://archive.cspo.org/rvm/publications/pubs_docs/Dietz_Research_Policy.pdf), so a particularly high rate might be indicative of someone *not* in academia.
Upvotes: 4 [selected_answer]<issue_comment>username_2: FWIW my university's promotion and tenure guidelines were revised recently (April 2013) to explicitly include consideration of patents and commercialization activities:

>
> "Promotion and tenure require excellent performance and the promise of continued excellence in 1) teaching, 2) service, and 3) research, creative work, and scholarship. The University values an inclusive view of scholarship in the recognition that knowledge is acquired and advanced through discovery, integration, application, and teaching. Given this perspective, promotion and tenure reviews, as detailed in the criteria of individual departments and colleges, will recognize original research contributions in peer-reviewed publications as well as integrative and applied forms of scholarship that involve **cross-cutting collaborations with business and community partners, including translational research, commercialization activities, and patents**."
>
>
>

I think we may see more of this sort of thing as the funding landscape changes and the share of state funding for universities decreases further.
Upvotes: 3


<issue_start>username_0: What's a quick and useful reference, either in print or online, for writing numbers and reporting measurements in scientific texts? Something I could distribute to all new students, which would cover things they should be taught but do not always know, like:

* how to write big/small numbers in scientific form (1.78 10–12), including proper spacing?
* how to write units of measurement, in text and in abbreviated form? includes: when do you capitalize units, when do you pluralize them, etc.
* how to properly report significant figures, etc.
* when do you write variables in italics/roman/bold/underline?<issue_comment>username_1: I guess [these recommendations](http://publish.aps.org/author-information) by the American Physical Society might be just what you are looking for. In particular, Style and Notation Guide for Physical Review/Physical Review Letters deals with scientific numbers, units, figures/tables, and use of roman/italic fonts. (For some strange reasons, it requires a secured connection to see the file; in case you couldn't see it, there is an excerpt [here](http://www.student.nada.kth.se/~f99-mle/Public/Blandat/howToWriteMath.pdf)).
Upvotes: 3 <issue_comment>username_2: The [American Chemical Society](http://pubs.acs.org/doi/pdf/10.1021/bk-2006-STYG.ch011) offers an online version of its *ACS Style Guide*. While this used to be available only in print, it is now apparently being distributed free of charge—or at least is available at any university with a subscription to ACS Journals. However, since it's intended to be a resource for anyone planning to submit to an ACS journal, I suspect it's generally available. The link above will take you to the specific chapter on math and units of measure.
Upvotes: 2 <issue_comment>username_3: The SI unit brochure ([BIPM](http://www.bipm.org/en/si/si_brochure/)) is actually quite clear and well-written, and backs up many of its points with logical/physical justification. I would say the style guides (should) build on this.
Upvotes: 1


<issue_start>username_0: I am thinking of pursuing a degree in Biostatistics (instead of just Statistics).
I am wondering if such decision will limit my career prospect to the field of Biostatistics. Is it feasible to think that somebody with a Biostatistics degree can work as, say, a quantitative social researcher or a data miner? The reason why I am considering Biostatistics program is because I get to take an epidemiology course, which to me sounds interesting. However at the same time I don't want to limit my career prospect strictly to the field of biostatistics....

thank you :)<issue_comment>username_1: I'm assuming you have a math background? If so, I would say go with regular stats and see if you can gear your course of study more towards the medical side of things (by taking a course in epidemiology or other medically related courses, and doing a research project in the biomedical field). I think you will leave more doors open this way. I think that Biostats is generally geared more towards biomedical research. You collaborate with doctors and scientists. Taking statistics would allow you to go this route if you desire, but also leave doors open to the quantitative social research/data mining that you mentioned in your post. Then again, take what I say with a grain of salt. Do more research on each subject to find out what is best for you.
Upvotes: 2 <issue_comment>username_2: There is no single right answer. There is value in specialisation, and value in generalisation.

To do statistics well, domain knowledge is crucial. So, if your work is to be in, say, health epidemiology, then the more knowledge you have of how health interventions are measured, and the causal patterns, the better a statistician you can be.

On the other hand, tools developed by health epidemiologists are now being used in other fields (for example, this [energy epidemiology programme](http://energy-epidemiology.info/) that I'm involved with), so the analytic skills are portable - but you will need to pick up knowledge in any domain you move into.

Maybe you need to base your decision on whether you want the first job or two after you finish the Masters to be in biostatistics. If you study biostatistics, then you'll have some domain knowledge, your analytic skills will be best directed to the right tools, and if it's designed well, the Masters will give you some opportunity to network with potential future employers: it will have guest lecturers from such institutions, and it will have a research component that you can do in partnership with one.
Upvotes: 3 [selected_answer]


<issue_start>username_0: I am a math PhD student and on the postdoc job market for the first time. I would like to know how job offer (hopefully) comes and what I should keep in mind in accepting the offers. I am interested in the US job market.

The first question is: When do most job offers go around? I am aware of the [earliest deadline](http://www.ams.org/profession/employment-services/deadline-coordination/deadline-coordination) (most math department agreed with this).
So I think the early offers go around in January and only the best people get an offer in January. I would appreciate it if someone could tell me about your experience.

Is it appropriate to ask about my application after a certain time? I think once one accepts an offer, one cannot decline the offer. So I may want to send inquiry to the school I want to work for before accepting the offer from some other schools.

I also heard of short lists (for assistant professorship). Could anyone explain how short lists work (I have only vague idea of what they are)? And are there short lists for postdoc positions?<issue_comment>username_1: I can at least comment on the "short list" phenomenon—the other areas are outside of my purview.

A "short list" is the list of finalists for a position. These are the candidates who are invited to the campus for a formal interview. However, it can also refer to the final ranked list of candidates to determine in what order they will be invited to accept the position being offered.

In general, short lists are restricted to faculty positions and "competitive" postdoctoral fellowships (such as the named fellowships at the US Department of Energy national laboratories, and at most mathematics departments as well) that are done at a departmental level. For a traditional postdoctoral vacancy, no such list is likely to exist, because hiring is done by the professor whose grant is supporting the position.
Upvotes: 2 <issue_comment>username_2: Here are my two cents (I am faculty in a math department, and I've served on postdoc recruiting committees):

Most math postdocs are department-wide competitive positions (e.g. "RAP"). More rarely they are attached to specific profs on specific grants. This answer addresses the former case:

Offers do go out as early as late January but the process extends well into February and even March. I've heard of people getting offers even later, once the landscape has "settled" more.

A short list is an internal list of top candidates, so that if one rejects a postdoc offer, it's easier to decide whom to give an offer to next (by only considering people from the short list). Offers are not always given in order starting from the "best" candidate -- as you can imagine there is some game theory involved on both sides.

It's definitely appropriate to contact places upon getting an offer. You can write: "Dear [X], I got an offer from [Y] but would prefer to go to [X] instead. I have a deadline of ...". Lots of things often get decided under such circumstances. And you can send such an email to multiple places. I wouldn't do it to toy around, but only write to places you genuinely prefer over [Y].

Until you accept an offer, you can feel free to try to drag out the deadline, contact other places you prefer more, etc. Universities try to get the best postdocs they can, and students try to get the best positions they can. Everyone understands this is how it works. But once you accept an offer, you can't really change your mind.
Upvotes: 5 <issue_comment>username_3: Here's a little more detail about timing of math postdocs offers, since that seems to be a particular focus of this question. What I'll describe applies only to research-oriented postdoctoral positions offered by mathematics departments in the U.S. Tenure-track jobs and anything teaching-focused are on a somewhat different schedule, and positions outside the U.S. may be totally different.

Everything is synchronized by the [AMS common deadline](http://www.ams.org/profession/employment-services/deadline-coordination/deadline-coordination), because most or all of the top candidates (depending on how strict your definition of "top" is) will have accepted offers by then. In particular, just about every position will have been offered to someone by then, but many of those offers will be turned down. Most departments will not set an earlier deadline than the AMS deadline, but they nevertheless encourage earlier decisions if possible. When a position is turned down, a competent department will try to make another offer as quickly as they can. Who gets the next offer depends not just on relative rankings of candidates, but also on factors such as research specialty and game-theoretic issues such as perceived likelihood of accepting the offer. Administrative dysfunction may limit how quickly offers can be made, but it's not uncommon for a department to go through several rounds of offers in January.

In practice, it seems to work out more or less as follows:

A few people get offers in December, but this is rather unusual. You shouldn't worry at all if you have no offers as of January 1.

The people perceived as top candidates generally start to get offers in early January. If you make it a couple of weeks into January with no offers, then you probably aren't going to end up getting tons of offers, but you shouldn't feel discouraged. All you need is one offer you're happy with.

As January progresses, more and more offers are made. Quite a few candidates, but certainly not all, will get an offer in the second half of January.

If you do get an offer, you should immediately do two things. First, you should withdraw any applications to (or turn down offers from) places you are no longer interested in, so they don't waste time and so you don't tie up offers someone else is waiting for. It's reasonable to hold onto several offers at once if you genuinely can't decide between them, but you do this only when truly necessary. Second, you should write to any departments you would prefer to your current offers to let them know about your upcoming deadline. The competition may increase their interest in you, and in any case they need to know they'll miss their chance at you if they don't move fast enough.

If you haven't yet received an offer, the second half of January is a good time to make inquiries. You can do this yourself, but it's sometimes better if your advisor looks into it. (All you can do is express interest, but your advisor can put in a good word for you and try to find out how things are going through back channels.)

As the AMS deadline approaches, there's a flurry of activity. Many offers are turned down or accepted around then, and departments are eager to get their favorite candidates before they accept another job, so there's a lot of turn-over for offers.

If you make it past the first week in February without an offer, it's a bad sign. It's certainly not a disaster, and plenty of strong mathematicians have gotten offers later than that. However, this is the point at which you need to start taking action, with your advisor's help. You need to figure out which positions are still open, and make sure they are aware that you are still interested. It's really useful if your advisor can work his/her contacts to help figure out what might still be a possibility and to make sure you don't fall through the cracks.

The more times passes, the more you need to actively search for a job, rather than just waiting to see what happens. If you hit March, then you're in trouble. You can still find a job then, but you need to be open to possibilities you weren't originally hoping for. For example, there are often late job postings for unpredictable openings, such as a temporary replacement for someone on sabbatical or leave. In March, you should be applying for every new opening you would be willing to take (and you should have a backup plan for what happens if you don't get a job at all, to help you decide where to draw the line). This is also a good time to review options with your advisor. Could you delay graduation for a year, or spend the next year as a lecturer?

I don't mean for this to sound discouraging. It's certainly possible to get a job quite late in the season, and you might even get lucky and find a really good job. However, I think it's worth having an overview of what the plausible outcomes are at each stage.

TL;DR: If you are hoping for a research-oriented postdoc in math, don't worry until late January, worry somewhat in February, and worry a lot in March (but don't panic).
Upvotes: 5


<issue_start>username_0: I am interested in pursuing a degree in Computer Science. Universities I've reviewed have "Major In Computer Science" , but give "Bachelor's in Informatics or Computing". There is no "Bachelors In CS", I simply couldn't find it. So **are Informatics and Computing the same as CS ? or IT?**

When I was looking for programming jobs, degree requirements were mostly CS. If I go to the university I mentioned above (Major in CS, but Bachelor's in Informatics), will I be able to write in my CV that I have CS knowledge?<issue_comment>username_1: This greatly depends on the country the university is in, and the language spoken there. For example:

* In Croatia, *informatics* and *IT* are really roughly a same thing.
What you want to study, in the strongest technical uni in the country, would correspond to something maybe best translated as *computer studies* (with a possible specialization in *computer science*).

A *computer scientist* from Croatia speaking with another Croatian might get slightly offended if somebody called him an *informatician*.
* In France, the only term they have to describe computer science is *informatique*, and the person doing that for a living would be *informaticien*.

Many French people will be talking about "informatics" when speaking in English.
* When I personally speak in English, I would never mix the terms *computer science*, and *informatics*. If I wanted to refer to somebody who might not be doing research (any more) I might switch to *computing*, or be more specific with the field.

I might be biased since I'm Croatian and come from that culture, and I'm not fully sure what the difference between *IT* and *informatics* is, but there's a definitive difference between *informatics* and *CS* in my mind.

The bottom line would be: **don't look at the Universities title when choosing, look at their program instead**. Look for classes whose descriptions match your interests and skills you want to obtain. If the program matches with your interests, that that is most probably the right university for you (without talking about the Uni's quality right now).
Upvotes: 3 <issue_comment>username_2: Sadly, I believe the most accurate answer is: it depends on who you ask.

Examples: Where I live, there is [Whatcom Community College](http://whatcom.ctc.edu) which has a Computer Science degree that dates back to the 1990s or earlier. It was largely about "computer programming" (which I'm going to mix together with "software development" as basically describing the same thing). A local state university [WWU's Computer Science department](http://wwu.edu/cs) also focused on computer programming.

In contrast, WCC's "Information Technology" (a.k.a. "IT") focuses more on tasks about how to set up and maintain computer networks and already-created computer software.

However, when I became a college instructor, I met a co-instructor who had an older Computer Science degree from another state (California), and he was not a programming expert. His expertise was in handling computer networking and troubleshooting situations on networks that were already set up. When I spoke to some business leaders (managers/owners) and told them my opinion of Computer Science referring to programming and IT referring to implementations, the majority disagreed and felt like my terms were reversed.

[username_1's answer](http://academia.stackexchange.com/a/17196/30772) goes to show that people have different opinions. I would be inclined to go the safe route, and assume that Informatics, like the other terms, are actually not completely defined by a single centralized source that everybody agrees with. If you think you can get more specific based on some patterns or trends that you notice, I caution you to be careful because my experiences indicates that people don't universally use the terms the same way. So if you're making an important decision based on what you think the terms mean to some people, then verify before proceeding with any dependent assumption.

(Despite all that I just said, I've still noticed nationwide posts do seem to refer to skills like technical support, and upgrading/maintaining infrastructure like handling backups and network services as being "IT".)
Upvotes: 0


<issue_start>username_0: I'm writing a research paper in English but the topic is about a different country and the data is written in a different language, Arabic.

Should I translate the data into English?
If yes, should I include a version of the excerpt in the original language?
If yes, how?

Apart from using excerpts of data, I sometimes have to mention certain words of the original language such names of participants and research fields, but I do not know whether I should use transliteration, translation or both.

Thank you<issue_comment>username_1: It would depend how much your translation of the story makes a difference to your thesis. If it doesn't particularly matter with respect to the finer details, it would be sufficient to provide the entire story in Arabic and then give a synopsis in English. If it's more critical that the audience knows what each line means, then give each sentence with its English gloss.

If you wanted to go to the extreme, you would have an interlinear gloss, which means that each word, or even morpheme, is glossed individually, and then a free translation given at the end. Here's a Hungarian example (from the Leipzig glossing rules):

```
Gila abur-u-n      ferma  hamišaluǧ  güǧüna  amuq’-da-č.
now  they-OBL-GEN  farm   forever    behind  stay-FUT-NEG
‘Now their farm will not stay behind forever.’

```

In linguistics we use these literally hundreds of times in a thesis, because this is where our data really is. It's probably more depth than you'd be willing to go into, and I would advise against it unless the Arabic syntactic and morphological features are absolutely critical to your thesis. The conventions that most linguists use (and tweak to their own subject language) with respect to the names of various grammatical features (OBL and GEN, for example) can be found in the [Leipzig Glossing Rules](http://www.eva.mpg.de/lingua/resources/glossing-rules.php).

I think you'd be fine to select from either giving the entire story followed by a synopsis of whatever detail you'd prefer, or a line-by-line transliteration.
Upvotes: 2 <issue_comment>username_2: You should be concerned with more than whether readers understand the data: you need to present the data in the rawest form practical, when there is a possibility of error in rendering the data in an altered form. If the data were transcriptions of interviews conducted in Arabic, and was not generally available to readers, then the data should be presented in its original form. This allows diligent readers to verify your conclusions.

For practical purposes (assuming the readership isn't a fluent Arabic-speaking population), a translation should be provided as well. Depending on the amount of material involved, you might present consecutive texts, first the Arabic and then the translation. If there is a lot of material, it may be best to put the original text in an appendix, unless issues of translation need to be discussed in the paper. For Arabic, there is also the question of *how* to present the text, i.e. in Arabic script or in (augmented) Roman letters. For Classical and Modern Standard, there are standards that lead to interchangeability so it does not matter too much, but for Colloquial dialects it would matter how the data was created (did the subject write it, or was it spoken and someone transcribed it?). In the latter case, questions of accuracy are especially bound to come up (you can't write ث as "th" in a dialect where it is pronounced "t").

There is no need to translate personal names. If you intend to use a term such as مقهى repeatedly, it should be translated the first time and transliterated all times (maqhā).
Upvotes: 2


<issue_start>username_0: Most colleges and universities in the US base their admissions on high school grades plus a standardized test (SAT or ACT). To decide whether a particular piece of data X is a good admissions criterion, people seem to focus on how well X correlates with college grades. (This may be freshman grades or total college GPA. Some people think success in getting a degree would be more appropriate. For the purposes of this question, I don't think these distinctions are relevant.)

Isn't this correlation a completely incorrect way of evaluating X as a tool for admissions?

It seems like an apples-to-oranges comparison. A student who has low X is admitted to a nonselective school, where standards are low and they compete with other low-X students. Grades at this school measure how well they did based on the low standards prevailing there. Students who have high X are admitted to a selective school. A "B" grade at Berkeley is not the same as a "B" grade at Cal State Dominguez Hills. A high-X student goes to Berkeley and gets a "B" in calculus. A low-X student goes to CSDH and gets a "B" in calculus. This shows up in the statistics as a lack of correlation between X and grades, since the students differed in X but got the same grade. But isn't that misleading, since the grades mean different things at the two schools?

Is there some more logically justifiable statistic to use in evaluating whether X is a good admissions criterion?<issue_comment>username_1: A more meaningful study might ignore college grades, which as you point out depend on the type of institution, and focus on downstream accomplishments. It isn't just local and community colleges that distort the picture; several highly ranked Universities have A-no credit policies, so that everyone graduate with a 4.0. Many education schools are effectively A-no credit, with overall GPA's of ~3.9.
I'd like to know how many university professors had low grades or SAT scores, how many NAS members had low grades or SAT scores, and what the profile of CEOs, successful writers, lawyers, engineers etc. was. The purpose of college is not the production of am impressive transcript.
Upvotes: -1 <issue_comment>username_2: Define "effectiveness of criteria for admission". Collect data on the potentially relevant independent variables. Do statistical analysis on the resulting data set.

Be careful, you'll probably be considering just the people who were admitted, so the results will be skewed.
Upvotes: 1


<issue_start>username_0: I am making a statement in my dissertation based on a comment written in the Linux kernel source code. I would like to be able to reference this comment. At least I would like to be able to name the file to which I'm referring.

Is this (citing files in large pieces of source code) recommended?

What is the best way to go about doing this?<issue_comment>username_1: It's not uncommon in certain areas of CS to cite specific files. In your specific case, I would suggest that you include the git commit hash of the kernel.org sources in the citation, so that people can find the *precise* version your referring to.
Upvotes: 3 <issue_comment>username_2: According to the [MIT handbook](http://integrity.mit.edu/writing-code) in relation to writing code you should use the following when citing code.

* Generally, the URL and the date of retrieval are sufficient. Add more details if it will help the reader get a clearer understanding of the source.
* If you adapted the code, you should indicate “Adapted from:” or “Based on” so it is understood that you modified the code.

The page link above also includes the following in relation to open-source material

>
> When you use code from an open source project, you need both to attribute the source and follow the terms of any open source license that applies to the code you are using.
>
>
>

Finally, as always with citing, you should check with your Department to see if they have guidance or a citation style-sheet.
Upvotes: 2 [selected_answer]


<issue_start>username_0: As a beginner researcher, several years ago, I sent some (2-3) papers to a quality journal (with ISI IF). The papers were rejected.
Now I am preparing a manuscript, which is in my opinion much more mature.
In the meantime I had some papers published in other journals.
Now I am considering the same journal again, because of the topic.
Is it a good idea ? Or is there going to be some bias because of earlier rejections ?
Just to clarify: this is a completely new manuscript.<issue_comment>username_1: This is perfectly legit. You are not forbidden to submit ever again to a journal that has rejected one of your earlier manuscripts. Indeed, rejections happen for everyone. It is best not to take them too badly.

Now, it would be a different story if you resubmitted the same paper that was previously rejected, or a version with only minor updates. Sending an entirely new manuscript, or a much more mature version, is unproblematic.

>
> Or is there going to be some bias because of earlier rejections ?
>
>
>

Important journals get **many** submissions, the majority of which they have to reject. I think it is very unlikely that the responsible editor even remembers that you already had a paper rejected a few years back. And even if (s)he does, it is unlikely that (s)he holds it against you. As I said, rejection is something that happens to every researcher, at least now and then.
Upvotes: 5 <issue_comment>username_2: In addition to username_1's answer, which hits on most of the main points, I would add one further suggestion, if the manuscript is a revised version of a previously rejected manuscript.

As part of the cover letter to the editor, you should indicate that this is a resubmission of the previous paper. You should also outline **clearly** what has changed from the previous manuscript to the current one: what have you added or removed, and how the present manuscript is an improvement over the previous version.
Upvotes: 3 <issue_comment>username_3: Not only is this a good idea, but its relatively common, and a decent publication strategy overall. Given a particular topic is likely to have more than one journal, a way to decide where to submit is to submit it to the "top" journal that you think you have a shot at getting into (you shouldn't submit work that has no chance - its a waste of your time and theirs), and then letting the paper "slide" down the rankings as it gets rejected.

Rejection doesn't even imply that you submitted to the wrong journal, or that the work wasn't worth trying to get in there - the vagaries of editorial discretion, publishing cycles, and reviewers mean something might have had a legitimate shot at getting in but just missed this time. The next paper? Start the process over again.

Unless your submission is egregious enough to be *memorably bad*, the odds of the editors holding rejections against you, or even remembering you specifically, are pretty low.
Upvotes: 3


<issue_start>username_0: Do departments look at MOOC's certificates favorably during the graduate admissions process? Will it give me any advantage over students who don't have them if I am trying to enter a PhD program?<issue_comment>username_1: You're free to mention the certificate in your application. How much weight to give to it is presumably up to individual committee members. I think unless the certificate pertains to a specific skill that's in demand for the program, you shouldn't expect to get much of a boost from having one. At the moment, people don't have much experience with such certificates, and will be more inclined to look at indicators they know better, like grades and GREs. It's possible this will change a bit in the future, but it will likely move pretty slowly.
Upvotes: 3 [selected_answer]<issue_comment>username_2: If the MOOC certificate is in a closely related field, it will help. If it is in something like "Underwater Basket Weaving", it may not give you an advantage over anyone else, unless of course you are entering a program in Underwater Basket Weaving. Or something similar, such as Underwater Knitting.
Upvotes: 0 <issue_comment>username_3: What sort of advantage do you have in mind? MOOCs could help a little in addressing weaknesses in your application: if there's a standard course you were unable to take, then it could be helpful to be able to say you learned the material by other means, and a MOOC certificate might carry a little more weight than completely independent reading. However, on a scale from saying you read a book to getting a strong letter of recommendation, a MOOC certificate is much closer to saying you read a book.

Beyond that, it can't hurt to list MOOC certificates on your CV, but I doubt they'll make any difference. If you're going to graduate school, you should have spent time on many different sorts of learning: lots of formal classes, extensive discussions with peers, independent reading, and ideally working with a faculty member on something (a senior thesis, undergraduate research, etc.). Adding a few online classes is just not a big deal, especially if people are unsure of what the standards are. Compared with things like letters of recommendation or research experience, MOOCs disappear in the noise.
Upvotes: 1


<issue_start>username_0: I'm an undergraduate studying Mathematics & Philosophy in the UK, about to sit my third-year exams. My course is structured as a three-year self-contained BA and a fourth year 'integrated masters' with its own exams, which I automatically progress to as long as I get a [2.i classification](http://en.wikipedia.org/wiki/British_undergraduate_degree_classification) from my combined second- and third-year results.

I've not been a good student. My tutors consistently report that I'm clever and engaged but I've suffered real motivation and work ethic problems. My results in second year were poor, and while I'm reasonably confident I can get a 2.i in these exams I'm unlikely to achieve stellar results this year.

I'd like to have a shot at going on to further study. Obviously I would need to do well in my fourth year, and as well as is possible this year; that's a given. I'd like to know if anyone has any advice on other things I could do to 'rehabilitate' a potential application, both from the perspective of being a better candidate and with a view to getting reasonable references from tutors.

While I'd welcome general answers as well, for specificity the most likely further study I'd be applying to would be a masters programme (combination taught/research) in philosophy.<issue_comment>username_1: I can't speak to Philosophy programs so this may not be terribly useful to you, but in general, if your grades/marks are not stellar, and you exams are not either, then you should probably rely more on the power of recommendations from people who know your good traits well.

There are probably several ways to do this, and though I'm not sure about philosophy, in the sciences for example, a couple of years of volunteering in laboratory with one or two different researchers and making yourself invaluable to them can garner you major points.

I didn't do wonderfully in my studies either, and my exams were not fabulous, but I spent three years after graduating my undergraduate university volunteering in two different labs, and enrolling in evening courses to (a) try and do better in essential courses pertinent to my area and (b) demonstrate that I was serious about graduate studies because of my initiative in taking all these courses.

I ended up with three great letters and job opportunities, and later grad school (PhD) through working with these professors.

However, a word of caution: If your weaknesses really include motivation and work ethic, then post graduate study is not going to be fun for you. It's completely different than undergrad as there is way less structure, and you have to be very self-motivated, make your own deadlines, work all the time without people asking you to, and complete projects on your own. It's really hard. If you do decide that you can do it though, I suggest finding an advisor who will be more hands on.

Hope this helps a little!
Upvotes: 3 <issue_comment>username_2: I don't know the details about Phylosophy (the large majority of the population of this site seems to be STEM, unfortunately), so I will speak from my experience (first and second hand) in Physics and related fields.

Universities in the UK thrive to be fair and objective. Admissions are regulated by committees that put high weight in objective measurements: that is, your transcript mostly. Even if you had a brilliant last year in your master's, your bachelor record may hinder you to get a position, at least in the better universities. Spain is a more extreme case, where a score function of your academic record and, to a lesser degree, proven research experience (ie, publications, that very few undegrads ever get) is the sole parameter considered for a grant.

In other countries the system allows for a more flexible and subjective evaluation. Good memory and analytic skills may get you high undergraduate marks, but if you lack creativity and good thinking you will not be good at research.

In Sweden, the PhD student is normally chosen by the PI based on whatever criteria he decides. I believe the majority of them put high weight in the projects you have done as an undergrad or master, and how well they think you will be able to work together. Grades are not so important for many because until some time ago, most universities only have "pass" or "fail" marks. I know of cases of PhD students that were hired without their supervisors even looking at the transcripts, but had a very good masters project.

Bottom line: *if you were in STEM* (as I cannot know if this is extrapolable to you), if you apply yourself in the last year, you could have options in a lower tier (but perfectly reputable) university in the UK; but you would probably have better chances in other academic cultures. Improving your grades will always help (don't let them fall); but the best you can do now to make you show apart from the other candidates is to excel at your thesis.
Upvotes: 2 <issue_comment>username_3: I can say something about philosophy Postgraduate study in the UK. If you have a 2.1 By, you ought to be welcome onto a whole load of decent MA courses, though to be prepared for a major step up in expectations. Bristol and Edinburgh both have MAs in Philosophy and Mathematics, for instance. If this is where you want to go I'd email their PG contact in Philosophy, say you're on an exit velocity for a 2.1, though nothing spectacular, and would anything stop you applying?
Upvotes: 2 <issue_comment>username_4: Going straight from undergraduate work to graduate is not the only path (not sure what the alternative paths would be in philosophy exactly - but I can imagine they are many and varied). The years between undergrad and grad can certainly be used for reform. Recommendations can weigh very strongly.
Upvotes: 0


<issue_start>username_0: When working on an assignment (e.g. essay, report), is it allowable or ethical to reference (as in both "read" and "cite") previous students' submissions for that assignment? For example, the student may have a friend or relative that sat the subject previously and is happy to share their assignments.

From my understanding, unless the teaching institution has an explicit policy, this should be allowable assuming the referencing is done correctly. So saying, it may not be the best educational experience, as the student may not undertake the critical thinking and research the assignment expects.

To be clear, I am not talking about referencing assignments of students currently studying that subject. That is clearly collusion.

I also assume the student attained previous submissions with consent from all the authors. However, I do not assume that all students currently working on the assignment have access to all past submissions.<issue_comment>username_1: I think it is both ethical and within the rules (or at least it is within the rules at my university) as long as the reference is clearly cited.

Furthermore, it should also be fine with current students as long as the work is cited too.

For example for undergraduate experimental lab course I took during my 3rd year, I worked on an experiment with 2 other students working on the same experiment independently. We each had to write a project report on the results we obtained during the experiment.

Only one of us managed to get a particular dataset for a part of the experiment that was sensitive to conditions and difficult to get results on. In that situation we were able to used that student's data so we could discuss their result in our report (as long as we cited the person who actually obtained the dataset, of course).

In a seperate experiment I let someone use a java code I wrote for the analysis. This was also fine, because the person cited me when they used my code in their report.

I think if the handin were supposed to be completely independently written (e.g. a math problem sheet) it would be collusion to reference older (or current) work but that is generally acceptable to cite anything (past student work or otherwise) in work which you would normally expect to see citation (e.g. essay or report).

If you have assessment regulations, there is probably a clear indication of what your university expect in there. That would not be able to tell you the answer to ethics part of this question but otherwise might be able to tell you the rule at your College or University.
Upvotes: 3 [selected_answer]<issue_comment>username_2: I agree with username_1's answer as far as it goes; I don't think it's *unethical* to reference work done by past students in your own work.

However, my worry would be that, except perhaps in certain specific environments, "work done by past students" is hardly authoritative. Courses for my degree, at least, only accept certain types of sources, mostly published books, certifiably authoritative websites, and of course journal articles.

In other words, I don't see a problem with reading past work, and of course mentioning that you have read it, but I personally wouldn't be comfortable with *citing* that work as a source.

Of course, this becomes less applicable the further up the education system you go; if you're writing a dissertation, you'll almost certainly be referencing past dissertations.

---

That said, the important question here, on a practical level, is whether or not *your instructor* thinks that reading or citing past students' work is ethical, not whether it actually is.

I'd advise caution; an email to whoever assigned the work, or a trip to their office, is more than worth the effort, considering the possible consequences if they eventually do decide that this is collusion.
Upvotes: 2


<issue_start>username_0: I am an Indian undergraduate student completing my BTech in Industrial Engineering in one of top three universities in India. I am planning to move on to physics for graduate school and I have a proper profile for it with research projects and internships in physics. I am confident I would get accepted to a good graduate program for physics, probably in the United States.

After browsing through the forums I found, it appears that a masters or PhD in physics is not a very lucrative option in terms of career. I in fact read that people are still in search of jobs after their PhD for three or four years. I also heard engineers have significantly greater career security after their graduation. So I am considering a double major in Physics as well as in Engineering to increase my future job prospects. I am determined focus on Physics in my masters program so it seems I should either to major in Physics and minor in Engineering or major in both. My primary job preference would be in the field of Physics academia, but wouldn't mind shifting to other areas.

*What is the best choice of undergraduate major/minors to provide me the best career prospects?*<issue_comment>username_1: I have heard that it is challenging to find academia jobs in physics in the US. However, industry may be very different. Financial firms, scientific modeling, consulting, and tech companies are very open to hiring physics grads. Having the degree shows that you're intelligent and can think critically, though you wouldn't necessarily be working on physics projects. Having another degree in engineering or cs would only help.
Upvotes: 2 <issue_comment>username_2: At least in Europe, and probably in the US too, physicists are highly considered in industry, the unemployment rate is almost a technical zero. Doing research is another different beast: there are not so many positions, and you have to fight for them. Also, Academia not a very lucrative career path: a first job for a physicist or an engineer in industry may grant them 3-4 times more than in grad school (depending on the conditions), and it continues onwards.

I see you are eager to study both degrees, which is commendable, knowledge never hurts. What you need to consider is the costs, both economical (can you afford the university?) and in effort (can you really do both degrees at the same time?). Both degrees are difficult, and will take a great emotional toll.

Some universities offer double degrees, where you end having both titles, and the subjects are (hopefully) sorted and arranged in the most convenient way for the student. For example, in Physics and Maths you need linear algebra; in a double degree you would only take the courses from Maths, as the ones in Physics are a subset. If the university does not have this arrangement, you may find yourself doing the same subjects twice, that may lead to added frustration.

Another cost are the grades: doing two things means you cannot do them both so well. Some admission put hard thresholds on the grades, and they may or may not consider your second degree as a sufficient merit to lower it. On the other hand, and provided the grades are not very damaged, almost any employer will see it as a great CV boost and will make it easier to get an industrial job.

As a last advice: if you go for it, be aware of your limits and ready to drop one of them if things get difficult before it brings you down. A degree and a half is a merit, two three-quarter degrees are two failures.
Upvotes: 2


<issue_start>username_0: As a sort of follow up to a [previous question](https://academia.stackexchange.com/questions/19803/ms-in-pure-maths-to-phd-in-statistics), what is advice you would give for telling my MSci advisor I am switching disciplines (pure maths to statistics) for doctoral work? I will presumably ask him for a letter of recommendation. I do not want to come across as ungrateful for his work with me, but the direction of my current research with him has come to bore me. How do I tell him that is a major reason I am switching disciplines? Do I even need to disclose such? I am closing in on completing my work with him, so anything dealing with changing advisors right now is not an option.<issue_comment>username_1: Directly.
---------

"I've decided to pursue a PhD in statistics."

Then after the inevitable discussion:

"Can you write me a strong letter of recommendation?"
Upvotes: 4 [selected_answer]<issue_comment>username_2: In many countries in Europe (France for example), statistics is a part of the Maths department. Theoritical statistics uses a lot of sophisticated mathematics, like measure theory, or functional analysis. A PhD in applied statistics would probably require a serious theoritical research, together with modelisation and programming.

So you should not be ashamed to switch to statistics. Tell your advisor this is not because of him, but due to your personal interest. Excellent scientist are driven by passion!

You may start the conversation by telling your advisor you're switching to physics. If he survives the shock, tell him you were joking and you are "just" going to statistics ;)
Upvotes: 1


<issue_start>username_0: I'm an individual interested in multiple academic disciplines such as neuroscience, biological engineering, computer science, history, anthropology, psychology, political theory ... to name just a few. Currently, I have a normal (40 hours/week) engineering job that leaves me enough free time to follow these topics to a moderately satisfying degree.

However, primarily due to intellectual curiosity, I'm considering doing a PhD (in Europe) and would ideally want to move to research later (discipline: something along the lines of genetic engineering, computational biology or cognitive neuroscience - not sure yet, but my background is in computer science). Given the highly specialized nature of modern scientific research that seems to demand intense working hours, one concern that I have is the **fear of becoming highly one dimensional**: completely failing to follow progress in other fields and in society as a whole, with publish or perish environment creating an inescapable feedback loop, where one cannot afford to read articles and books on topics not related to work, or follow public debates; yet alone write a blog about e.g. social issues.

I realize there are academics out there who seem to be prolific across multiple disciplines (e.g. Chomsky, Pinker ...), but the question still stands: **in a modern research environment, can one satisfy multiple intellectual interests that transcend immediate work requirements**, or has the game become too competitive and focused for that? What are the main variables?<issue_comment>username_1: You can do it, but you might not be a professor. I have a PhD in Aerospace Engineering, and I work in CFD, HPC clusters, and BigData/analytics when I do research. My primary job is running the HPC department at a large-ish computing center. If I were a professor, I doubt I could pull this off. My research output is much smaller that professors my age, but I still have a pretty satisfying career.

You best chance for doing lots of different kinds of research is to find a center who appreciates you for your broad interests and to help it grow its mission while pursuing your more esoteric interests when you can.
Upvotes: 4 <issue_comment>username_2: The answer may vary across field and institution and continent. But based on my experience, in short, I would say: Yes, a modern research environment permits and even benefits from broad intellectual interests.

But first let's draw a distinction between what I'll call your "output" and your "input". For most researchers, research output is highly specialized. It can take an intense focus to make progress on the hardest, most impactful problems (<http://matt.might.net/articles/phd-school-in-pictures/>). Not only that, your progress is generally a super-linear function of the time you put into a project. You invest in learning past work, concepts tools, developing ideas often over a period of multiple years -- and then connections happen faster, ideas mature, you know the software tools, and overall you produce much more quickly. Good interdisciplinary work is possible and does allow you to use expertise in multiple areas or fields; but, in my experience, this work can be just as specialized.

Your "input", however, is an inspiration for what research to tackle. I find that my creativity in producing good research ideas is substantially better when I explicitly take time to step back and think, interact with new people, and explore new concepts. And the best researchers have a good understanding of society and gut feeling about what will be really impactful in the next few years. If you completely fail to follow progress in your field, in other fields, and in society as a whole, you might produce more papers in the short run but probably have less impactful research in the long run.

As [Hamming said](http://www.cs.virginia.edu/~robins/YouAndYourResearch.html):

>
> Another trait, it took me a while to notice. I noticed the following facts about people who work with the door open or the door closed. I notice that if you have
>  the door to your office closed, you get more work done today and tomorrow, and you are more productive than most. But 10 years later somehow you don't know quite know what problems are worth working on; all the hard work you do is sort of tangential in importance. He who works with the door open gets all kinds of interruptions, but he also occasionally gets clues as to what the world is and what might be important. Now I cannot prove the cause and effect sequence because you might say, "The closed door is symbolic of a closed mind." I don't know. But I can say there is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing - not much, but enough that they miss fame.
>
>
>

Finally, as a professor, you have a great deal of freedom to steer your own research agenda; more than in most professions. While it's hard to make progress in wildly divergent fields concurrently, over longer timescales (5-10 years) many researchers do change their focus area quite successfully.
Upvotes: 4 <issue_comment>username_3: I know this question was asked a long time ago, but for future reference: I think the answer is "kind of."

The best way that I know to do this professionally is to become a "method development" person. In other words, if your job is to develop new statistical or computational tools, or to develop new instrumentation to measure things that weren't previously possible to measure, *and* if you are very good at striking up collaborations across different disciplines, then I think it's totally possible to remain somewhat agnostic about what exact questions you want to pursue using your new methods.

(This is adapted from something a previous advisor said to me, which is that you can be a "methods" or a "problem" person. At the "problem" end of the spectrum, to contrast with the above, you would be motivated by a really specific question, would understand it better than anyone else, and would be more willing to use a wide variety of methods to study it. It can be a good exercise to look at people's careers and try to see where they fall on this spectrum.)

The caveat is that in order to do this successfully, you're still going to need to go "narrow and deep" at the PhD level in something like applied math, statistics, algorithms, etc.; otherwise it's going to be very hard to do work that is cutting-edge enough to genuinely matter across a variety of applications. Doctoral work is all about making a unique contribution; if your knowledge doesn't take you "right up to the edge" of any field, that's going to be really difficult. You will also likely have to cede a little control in terms of choosing questions, because you won't always be able to shoehorn an existing interest around your analytic specialty. But if you want a lot of variety in a tenure-track academic career, I think this is your best bet.
Upvotes: 2