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Drafted: BOS / 2006 NHL Entry Draft
Round: 1st (5th overall)
Twitter: @PKessel81
Favourite Movie: Boondock Saints/Rounders
Favourite offseason activity: Golfing/Fishing
Best part of playing in Toronto: The fans and the trainers
BECAME A LEAF: Acquired from Boston for Toronto's first (Tyler Seguin) and second (Jared Knight) round choice in 2010 Entry Draft, and first round choice (Dougie Hamilton) in 2011 Entry Draft, September 18, 2009.
TRANSACTIONS: Originally Boston's first choice, fifth overall, in 2006 Entry Draft.
2011-12 SEASON: Played 82 games for the Maple Leafs collecting 82 points (37 goals, 45 assists) with 20 penalty minutes. Ranked 6th (tie) in NHL in goals and 6th in points. Ranked 1st on Maple Leafs in goals, assists, points, shots (295), powerplay goals (10), powerplay points (23), game-winning goals (6). Established career highs in a season for goals, assists and points. 1 of 2 Maple Leafs players to play in all 82 regular season games. Scored 1st Maple Leafs hat-trick and collected an assist Oct. 8 vs. Ottawa. Registered a 5-game point streak (Oct. 6 – 19) to start the season with 12 points (7 goals, 5 assists). Named NHL's First Star of the Week (Oct. 17) with 8 points (5 goals, 3 assists) in 3 games. Named NHL's First Star of the Month (October) with 18 points (10 goals, 8 assists) in 11 games. Became 1st Leafs player to be named NHL Player of the Month since Felix Potvin (Oct. 1993). Registered a career-high (27:07) of ice-time Nov. 17 at Nashville. Played 400th career NHL game Dec. 3 at Boston. Scored on a penalty shot and collected an assist Jan. 7 vs. Detroit (Jimmy Howard). Registered a season-high 8-game point streak (Dec. 22 – Jan. 7) with 9 points (5 goals, 4 assists). Played 200th career Maple Leafs game and scored a goal Jan. 23 vs. NY Islanders. Collected 3 points (1 goal, 2 assists) for Team Chara at 2012 NHL All-Star Game Jan. 29 at Ottawa. Collected 3 points (1 goal, 2 assists) and a (+3) rating Feb. 4 at Ottawa. Collected 3 points (2 goals, 1 assist), including 300th career point, and a (+4) rating Feb. 6 vs. Edmonton. Collected an assist and set a career-high with 65th point of season Feb. 23 vs. San Jose. Scored career-high 37th goal of the season Mar. 31 vs. Buffalo. Tied a Maple Leafs franchise record with 3 assists in a period, while collecting 100th career Leafs assist and 200th career Leafs point Apr. 3 at Buffalo. Scored 1 goal on 7 shootout attempts. Won Maple Leafs' Molson Cup Award for the third consecutive season.
CAREER: 2010-11: Played 82 games for the Maple Leafs collecting 64 points (32 goals, 32 assists) with 24 penalty minutes. Ranked 1st on Maple Leafs in goals, points, shots (325), powerplay goals (12), powerplay points (23), game-winning goals (6), 1st (tie) in overtime goals (1) and shorthanded goals (1). Ranked 2nd in assists. 1 of 5 Maple Leafs players to play in all 82 regular season games. Established career highs for powerplay goals, shorthanded goals and shots. Ranked 4th in NHL shots. Scored 2 goals, including game-winning goal in overtime , 100th career NHL goal and added an assist Oct. 15 at NY Rangers. Played 300th career NHL game and scored a goal Oct. 26 vs. Florida. Scored 1st career shorthanded goal Nov. 26 at Buffalo. Collected 200th career NHL point with goal and an assist Dec. 14 at Edmonton. Selected to play in 2011 NHL All-Star Game Jan. 11. Collected 100th career NHL assist Jan. 24 at Carolina. Tied a career-high with 4 points (2 goals, 2 assists) Feb. 24 at Montreal. Named NHL's First Star of the Week (Feb. 27) with 8 points (4 goals, 4 assists) in 4 games. Finished season on a 10-game point streak (Mar. 19 – Apr. 9) with 11 points (4 goals, 7 assists). Scored 2 goals in a game on 5 occasions (Oct. 15, Dec. 28, Jan. 6, Feb. 15, Feb. 24). Collected 2 assists on 6 occasions this season (Nov. 16, Jan. 1, Jan. 20, Feb. 24, Feb. 26, Mar. 31).
2009-10: Played 70 games for the Maple Leafs collecting 55 points (30 goals, 25 assists) with 21 penalty minutes. Ranked 1st on team in goals, points, powerplay goals (8), game-winning goals (5) and shots (297). Ranked 2nd (tie) on team in assists. Ranked 6th in NHL shots. Missed 12 games (Oct. 1 – 31) due to shoulder injury. Made Maple Leafs debut and recorded 10 shots Nov. 3 vs. Tampa Bay. Collected 1st Maple Leafs point (assist) Nov. 6 at Carolina. Scored 1st Maple Leafs goal and added an assist Nov. 7 vs. Detroit (Chris Osgood). Registered a 6-game point streak (Nov. 6 – 17) with 8 points (5 goals, 3 assists). Named to Team USA for 2010 Olympic Winter Games Jan. 1. Registered a team-high 7-game point streak (Jan. 9 – 21) with 8 points (1 goal, 7 assists). Registered a 5-game point streak (Jan. 30 – Feb. 8) with 10 points (6 goals, 4 assists). Won a silver medal with Team USA at the 2010 Olympics, collecting 2 points (1 goal, 1 assist) with a (+2) rating in 6 games.
2008-09: Played 70 games for Boston collecting 60 points (36 goals, 24 assists) with 16 penalty minutes. The 36 goals scored were the most by a Boston player since 2002-03. Registered 14 multi-point games. Registered 2nd career hat-trick and a career-high (tie) 4 points Apr. 12 at NY Islanders. Registered a career-high 18-game point streak (Nov. 13 – Dec. 21) with 28 points (14 goals, 14 assists). That was longest point streak in the NHL, tied him with Ed Olczyk for the longest point streak by a U.S. born player in NHL history, the 5th longest in Bruins history and the longest by a Bruin since 1996-97. Registered a career-high 6-game goal streak (Nov. 26 – Dec. 8) with 7 goals. Collected 100th career NHL point (goal) Dec. 18 vs. Toronto. Played 200th career NHL game Feb. 7 vs. Philadelphia. Named NHL's First Star for the week ending Dec.14, collecting 8 points (3 goals, 5 assists) in 4 games played. Named NHL's Third Star of the week ending Apr. 12 collecting 6 points (5 goals, 1 assist) in 3 games played. Missed 12 games due to injuries (mononucleosis, shoulder and flu-like symptoms) and underwent shoulder surgery May 21. Played 11 playoff games for Boston collecting 11 points (6 goals, 5 assists) with 4 penalty minutes. Ranked 3rd on Boston in playoff points.
2007-08: Played in a career-high (tie) 82 games for Boston. Registered 1st career hat-trick and career-high (tie) 4 points Oct. 12 at Los Angeles. Registered 5 shootout deciding goals, which led the NHL. Made NHL playoff debut and collected 1st career playoff point (assist) in GM #1 of ECQF Apr. 10 at Montreal. Scored 1st career NHL playoff goal in GM # 5 of ECQF Apr. 17 at Montreal (Carey Price). Scored 2 goals in GM #6 of ECQF Apr. 19 vs. Montreal. Played 7 games for Team USA at IIHF World Championship collecting 10 points (6 goals, 4 assists) with 6 penalty minutes.
2006-07: Ranked 1st among all NHL rookies and 2nd (tie) overall in shootout deciding goals (4). Played 1st NHL game Oct. 6 at Florida. Collected 1st NHL point (assist) Oct. 7 at Tampa Bay. Scored 1st NHL goal Oct. 21 vs. Buffalo (Martin Biron). Missed 11 games (Dec. 11 – Jan. 4) following a testicular cancer diagnosis and surgery. Won the NHL's Bill Masterton Trophy for 'perseverance, sportsmanship and dedication to hockey'. Played 7 games for United States at IIHF World Championship collecting 7 points (2 goals, 5 assists) with 6 penalty minutes. 2005-06: Played 39 games for University of Minnesota (WCHA) collecting 51 points (18 goals, 33 assists) with 28 penalty minutes. Ranked 2nd on team in points and 6th in the WCHA. Ranked 13th in NCAA scoring and 1st among freshman players. Ranked 1st on team with a (+22) rating. Named WCHA's Rookie of the Year and WCHA All-Rookie Team. Scored 1st career college goal on a penalty shot Oct. 21 vs. Minnesota St. – 1st time in team history that had occurred. Played 7 games for Team USA at IIHF World Junior Championship collecting 11 points (1 goal, 10 assists) with 2 penalty minutes. Ranked 1st in tournament scoring. Played 7 games for Team USA at IIHF World Championship collecting 2 points (1 goal, 1 assist) with 2 penalty minutes. Was only college player on Team USA at World Championship. 2004-05: Played 7 games for Team USA at IIHF World Junior Championship collecting 6 points (4 goals, 2 assists) with 2 penalty minutes.*Holds USA Hockey's record for career points at the IIHF World Under-18 Championship with 26.


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LOMA MAR -- What if, instead of worrying whether your mom had sewed name tags into your underwear for summer camp, you had to wonder whether she would still be alive when you got home? Or what if you were 6 years old and got packed off to "Cancer Camp" by your grieving father not long after your mother died of the disease?
That's what happened to Kieran Ward, of Menlo Park, now 13, who had to get on a waiting list seven years ago before he could get into Camp Kesem at Stanford, the first of what has become a nationwide network of sleepaway camps for kids ages 6 to 16 with a parent who has, or had, cancer. Expecting to have to knit chemo caps in arts and crafts, a week in the woods with cancer campers was not Kieran's idea of a good time.
"I came because my dad talked me into it," he said, his blond hair and porcelain skin painted green for the camp's annual Song Party. He was sullen and withdrawn after his mother, Ginger, died when he was 3 ½. "I thought I would come here, make no friends and be sulking around in a corner a lot. But they make everything so spirited, so fun, that it sort of softens the blow of all the bad emotions I felt. Now I wait every year for this."
Camp Kesem, which gets its name from the Hebrew word for "magic," is a transformative experience for most of the campers, who spend a week between Woodside and La Honda, amid the redwoods of the Santa Cruz Mountains.
"A lot of them resist it before they come," said camp director Heather Paul, "and then they get here and find out it's not a cancer camp."
Unlike other camps, which often forbid phone calls to and from parents, Camp Kesem has a hotline specifically for that purpose.
"For a lot of them, the homesickness can be intense," Paul said, "especially if the parent was recently diagnosed."
A little haven
There had long been camps for children suffering from cancer and other life-threatening diseases, but until Stanford University's Hillel organization started Camp Kesem 13 years ago, there was almost no place for kids from families stricken with the disease to turn when they needed a break from watching their parents' suffering.
"Kesem is a little haven where everyone understands and supports each other," said 15-year-old Juliane Bombosch, of San Bruno, whose mother was diagnosed with stage 4 breast cancer. "It was very hard on the family to see her sick because we love her so much. I think what's traumatizing is not necessarily the chemo or radiation. It's seeing someone you love in that much pain, and that they're fighting to be there for you. And there's nothing you can do."
Juliane this year is attending her fourth Kesem, and among the 120 campers and 75 counselors -- all undergraduate volunteers from Stanford -- are many who keep coming back until they exceed the age limit.
Camille Brown, a senior majoring in information technology and media in society, became a counselor because she was too old to be a camper when her mom was diagnosed with colon cancer. Dealing with her mother's illness forced her to grow up fast, a process she tries to undo at camp.
"I didn't want to be a burden for my mom when she was going through second, third and fourth recurrences," she said. "I felt like a 40-year-old just trying to get through it. I had no one to clue me in it was OK to act like a 7-year-old sometimes."
The counselors all go by "camp names" that reveal some aspect of their personality. Brown's is Chameleon. At Camp Kesem, she peels back the 40-year-old and gets to act like a little girl again. But when one of the campers wants to talk about the stresses they face at home, she and the other counselors are always ready to listen. Many counselors have cancer stories of their own.
Transformative experience
Every year, Wednesday night is devoted to a ceremony called Roots, during which cancer is addressed explicitly. Everyone walks into the dining hall carrying a single long cord, which is cut into small pieces, then turned into bracelets. "There definitely are kids who start to break down when they're telling their story," said Jeremy "Pongo" Kim, who will be a senior majoring in electrical engineering. "It can be tearful, but no one's ashamed to cry."
After that catharsis, the camp pivots emotionally. "There's something really special about Thursday morning," Paul said. "There's a transformation that happens overnight as a result of that program. It's really nice."
There are 37 Kesems nationwide, with another five expected to be available next year, but because cancer is expensive, the camps remain free of charge -- and fundraising is a constant challenge. Counselors usually remain in touch with campers in their unit throughout the year, and when the worst happens, they attend memorial services and funerals.
Every camper processes the experience differently; some go to forget about cancer, others to finally be allowed to talk openly about it.
"When I'm at home, I try to shut it off because I want to make sure my mom is happy, and not worrying about me being sad," Juliane said. "I want to make sure she knows I believe in her. When I get here, I can relax and let it go and let everyone kind of be there for me. It's actually the time when I grow most. I feel I've learned a lot about who I am here because I feel so safe."
Contact Bruce Newman at 408-920-5004. Follow him at Twitter.com/BruceNewmanTwit.
how to support camp kesem
Find a camp at www.campkesem.org, or make a donation online at www.campkesem.org/donate and designate Stanford as your campus giving choice. Or mail a check to "Camp Kesem at Stanford" at P.O. Box 20526 Stanford, CA 94309.

subset: commoncrawl


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Ever since 2009, Eagle County's budget discussions have had a consistent refrain - revenues are down and expenses need to follow suit.
And during the 2013 budget presentation Tuesday, county finance officials advised the Eagle County Commissioners that the tune isn't likely to change in 2014.
During Tuesdays hearing, Eagle County Manager Keith Montag was joined by County Finance Director John Lewis and County Controller Tom Hyatt to present an overview of the proposed 2013 spending plan. The actual budget adoption is slated next week.
As they structured the spending plan for the year ahead, the county officials noted the challenges were concrete - property tax revenues are projected to drop 20 percent or $3.2 million and the county's other revenue sources are not rebounding quickly enough to cover that loss.
Montag noted that while sales tax receipts actually increased 8 percent during 2012 and are anticipated to grow another 2 percent during 2013, the county's licensing permits and fees dollars are showing a much less dramatic increase and interest income is slated to grow by only $100,000 in 2013. That's a particularly noteworthy figure considering that the county's investment income has dropped 79 percent, or nearly $3.6 million, since 2007.
With a bit of wiggle room provided by healthy fund balances, the county finance team figured it needed to cut operating expenses by approximately $1.6 million for 2013. Montag said that means following a six-point program:
• Examine all programs and related expenses.
• Ask departments to increase/find revenues opportunities and decrease expenditures.
• Continue the soft hiring freeze.
• Fill vacancies from natural attrition only after approval.
• Improve accuracy of cost allocations
• Work with health care costs to change funding levels for improved claims.
"I am very delighted to report that for 2013, the Eagle County budget is balanced. We had some challenges," said Montag.
The latest 30 percent drop in property tax revenues is only the latest hit to the Eagle County operating budget. Since 2009, annual operating revenues have dropped from $41 million to $30 million. With revenues drying up, the county has cut operating expenses by $12.6 million.
A large portion of those dollars came from the county's payroll. In 2008, the county employed 312 full time equivalent (FTE) workers. That figure has dropped to 235 FTEs in 2013, down from 238 FTEs in 2012.
The largest portion of the $2.6 million operating expenses cuts came from the county's Health and Human Services Department. This arm of county government cut 3.5 FTEs and reduced its overall budget by $777,000.
While the biggest theme of the 2013 operating budget is cutting expenses, the county has earmarked $518,000 for a 3 percent merit pool, which means employees will be eligible for raises. Last year the budget included a 4 percent merit pool and it was the first time in four years that the county offered raises.
During the budget presentation Tuesday, county officials noted that while much of the attention is centered on the operating, or general fund, there are actually a number of other county funds that make up the entire budget. Some of these funds have their own property tax mill levy or earmarked sales tax revenues (such as the county's open space fund or the ECO transit fund) while others are enterprise or independently funded (the solid waste disposal department or the Eagle County Airport) that can not be applied to other county operations.
In the case of some funds, the open space fund for example, the county has traditionally saved revenues for a number of years and then spent the dollars on larger land purchases. Additionally, grant funds are often allocated to the county's non-operating funds, which produce one-time revenue and expenditure peaks.
When taking the entire operation into consideration to include the non-operating expenses, the county is looking at $90 million in revenues and $84 million in expenditures during 2013.
As part of developing a 2013 budget, the county is looking ahead to 2014 and 2015. A property reappraisal is slated for next year and the county is forecasting an additional $2.3 million drop in revenues.
That's one of the reasons why it was critical to cut spending in 2013 to avoid an even deeper hole next year, said Montag.
"Because property values run in cycles, the next one will hit in 2014 and we don't want to want to wait until 2014 (to address the anticipated revenue drop)," Montag said. "We did make changes and we are closing that gap.
Tuesday's hearing won't be the last time the county commissioners visit 2013 budget issues. Next week will be the formal adoption of the plan and consideration of how to allocated the $240,000 earmarked for community grants. Additionally, the commissioners will make final decisions regarding how to allocate capital improvements dollars in January.


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July 4th weekend a great success
Business Short Takes
July 17, 2010 - By Wayne Sheets Contributing Business Writer
Last week I touched on the unpleasant things that happened during the July 4th weekend celebrations. Today there are mostly good things to talk about....
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Piaggio is one of the few major powersports vehicle manufacturers already offering product featuring alternative powerplants. The company's new hybrid-drive scooters feature a "parallel" drive system in which an internal combustion engine and a zero-emissions electric motor are mechanically and electronically linked and simultaneously supply power to the wheel.
Piaggio (located at Dealer Expo in the Italian pavilion) first revealed its Hybrid Scooter (HyS) technology in 2004 at the Intermot trade fair in Germany. The manufacturer's latest HyS offering comes in the form of the 2008 MP3 HyS.
The Piaggio MP3 HyS has all the normal controls found on a scooter plus a special switch to choose from one of three operating modes: two hybrid modes and an electric mode. In the two hybrid modes, the HyS system controls power output from the internal combustion engine and electric motor using ride-by-wire engine management. An electronic management system interprets the rider's requests for more power and selects the assist ratio according to the state of the system (e.g., the level of battery charge). During deceleration and braking, the control system recovers and accumulates power. In electric mode, the HyS system shuts down the combustion engine to create a silent, zero-emission electric vehicle.
The traction batteries are hidden in an under-seat storage space, which is nevertheless still big enough to hold a helmet and other personal effects. A battery charge indicator on the dashboard keeps the rider informed of battery charge level. The batteries can be recharged via a normal wall socket. Charging time is about three hours. — Guido Ebert


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9:30 pm
Lidia's Italy In America "California: Pasta - The Ultimate Showcase" Lidia returns to California to visit Andy Boy farm and learns all about broccoli rabe. Later, Lidia meets up with Dino Cortopassi, owner of Corto Olive, to hear his family's colorful culinary history. Join Lidia and Grandma in the kitchen as she makes Baked Stuffed Shells, Penne with Artichokes, and Bacon Orecchiette with Andy Boy's Broccoli Rabe. D
10:00 pm
Cook's Country from America's Test Kitchen "Not Just for Kids" Bridget shows Chris how to make the ultimate Chicken Nuggets at home. Then, Jack challenges Chris to a tasting of yellow mustard. Next, Adam reveals his top picks for broiler-safe cooking dishes. D
10:30 pm
Simply Ming "Shiso & Tomatoes" NULL
11:00 pm
Taste of Louisiana with Chef John Folse & Co.: Our Food Heritage "Native America: Native Plant Foods" The Lower Mississippi Valley provided over 250 natural herbs for the Native Americans. Kim Hollier and Dr. Charles M. Allen of the Louisiana State Arboretum in Ville Platte give us a firsthand look at these wild edibles. Dr. Alma Blanchard, a "traiteur," explains the art of this alternative medicine practice and even treats Chef Folse for an old injury. D
11:30 pm
Rick Steves' Europe "Helsinki and Tallinn: Baltic Sisters" Rick explores the capitals of Finland and Estonia to sample each country's history, art and distinct love of life. In Helsinki, he finds a neoclassical old town, modern flair for design and steamy saunas. Tallinn offers medieval charms and new-found prosperity.G


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Tottenham Hotspur Message Board
not_blonde_really not_blonde_really Oct 13, 2012 16:20 Flag
Bit early to be talking about next week's game I realise, but I've come across this which is quite interesting:
Bit of background on the history between the clubs (e.g. did you know we played each other 16 times during WW2? And other obscure facts), as well as For & Against total numbers etc.
The short version is we pretty much end up even over the last 330-odd games, Chelsea have a slight edge (our record against them is apparently 48.67%) but there's everything to play for folks!Sort: Newest | Oldest | Most Replied Expand all replies
not_blonde_really not_blonde_really Oct 24, 2012 14:18 Flag
I don't think it's a fitness thing for Hudd either (well, I think it's match fitness rather than general fitness), his game is much more Scholes- or even King-esque in that it's about precision and positioning. You'd never expect him to out run Lennon but you'd expect him to have a more accurate cross.
For me it's whether or not that fits in a team that has Sandro, Parker, Livermore and Dembele (he used to do the box-to-box bit sometimes too) that's the question, and whether he'll retrieve enough form to oust any of them.
But then he's long been a player who's divided opinion!
Goes to show we all see things differently. I don't think we were awful after last Christmas bar 3 or 4 games. The way I saw it, it was the other way round. I thought we played ok, apart from 3 or 4 games. Norwich (H), Woolwich (A) being the main examples of being poor in the league. BUT we didn't get the results when we did play well (unlike at Utd this season where we didn't play for large parts of the game, but got a good result).
Utd (H) - I thought we should have won as well as Everton (A) - and at least got a result at QPR.
T'Hudd - I think the man is ok. I don't think AVB would be playing the man if he thought he wasn't 'fit' or in shape - given Livermore was available - and AVB/Spurs were trying to offload T'Hudd - so to play him, must show some form of confidence in him.
The subs bench - isn't that the point of having a strong squad ie to cater for when you have injuries across the board? I don't think the thinning of the squad was AVB as such - that seemed like a Levy thing given the timings.
i dont think we have played that well for most of this season.
last season we were awful from xmas onwards, bar 3 or 4 games.
i dont get the ade story. he missed pre season and then he did his hamstring in. so he has only missed starting 2 or 3 games.
even then, we dont want to rush him back.
the thing is, we will be playing a lot of games so u could easily rotate between defoe & ade. some games even play them together. also both are going to pick up injuries.
so overall, they will both play lots of games.
i never thought hudd was in cracking form. for me, he does a couple of good long balls and the odd screamer.
the game has become so quick and more about the middle of the park. so to have him plodding about in the middle is a hinderance to our team.
he is paid a lot of money by our club which at the end of the day is hard earnt money spent by fans. yet he still cant be bothered to get in shape. i think he shows a huge lack of desire & commitment. we dont need players like that.
our bench was weak but thats because we had the following missing-
not_blonde_really not_blonde_really Oct 21, 2012 17:58 Flag
Thing is, I think that's fairly similar to last season; overall I wasn't that impressed by the performances last year but we always seemed nearly there.
According to some of the 'news' papers Adebayor's had crisis talks with AVB about not starting, but as all the commenters on their websites point out, his protracted transfer meant he didn't have a proper pre-season so his fitness was low, then he got a little injury, so it's only a few games that he's just not been favoured for. It looks like AVB will play him in a similar position to Defoe's, and you never drop an in form striker, but he'll get his chance.
Re Livermore, he's trying to bring Hudd back slowly since he had such a big injury, so it makes sense to me that Livermore came on for him when he did. SB, I don't share your view on Hudd, I thought he was on cracking form 18 months ago but he spent a year out with injury, he'll be a bit rusty for a while - that's why AVB wanted to sent him on loan. We can't know at this stage whether or not he'll find it again, but fingers crossed he does.
John, I agree about the subs, it really worried me from the off; I quite often feel we need an attacking MF type player to come on around 65 mins to give the team a boost, and there were no 'game changers' on the bench.
Evening fellow Yids.
This is a must win game for us.
No good going to Old Trafford and winning and then losing in your own back yard to a bunch of over rated t0ssers who parked a 37 bus and won the champions league.
In the last two PL games against this lot we should have had all 6pts and so we need to put them to the sword.
When you live in the south of London like me,these fcukers are everywhere and totally get in your face with how great they are with all their money, how they deserve to be where they are.
Yes, this is going to be a tough test for us,but looking at player for player i think we can match them in all areas,and we have the home advantage in the FANS.
2-0 at half time and 3-1 on the final whistle.
Get in you Yids.
SPURS FOREVER COYS atb JEFFALL
- 1 Reply to JEFFALL
not_blonde_really not_blonde_really Oct 20, 2012 13:52 Flag
Squeaky bum time!
I reckon we need an injection of Adebayor, and for... some of midfield (dunno who) to be pulling the Chelsea players infield so Lennon can make more of an impact, he usually has the run of Cole.
It seems to me to be a largely fair game, fouls have been more mistimed than cynical, so 5 yellows seems like a lot for the whole match, never mind the half. Any idea what Tom's was for? & I thought Gallas got the ball.
However, I don't think it's a must win against the in-form side of the season so far, I think it's an ideally don't lose... obviously the better the result the better implications for us post-match, but it's not the end of the world if it a poor result.
He puts an awful lot of effort into that site.
I still don't follow this '...we always lose/win against them...' bit. How can that work, when it's a different team, different manager, different footballing philosophy? But it seems to - teams do have bogey teams.
But the good news is that over the past few years we've broken more and more of these hoodoos.
I'd still like us to play two halves of good football. Maybe this is the game.
I'm still a bit lost as to the changes that AVB was to bring in - the high line, the pressing etc. I don't see much difference to before, just a bit slower in the passing - but to me that was to be expected given the changes in midfield.
- 1 Reply to Jlock
not_blonde_really not_blonde_really Oct 18, 2012 20:58 Flag
Got to love yahoo, directly under the story titled "AVB not thinking about revenge" is one entitled "AVB out to avenge Chelsea exit"...
Berkshire Yid Berkshire Yid Oct 14, 2012 08:55 Flag
What a boring Weekend with no prem footie no Soccer Saturday no MOTD no Super Sunday :-(
Roll on next weekend
- 1 Reply to Berkshire Yid
A Yahoo! User Oct 18, 2012 15:49 Flag
NOT boring for me BY, I was in Tenerife enjoying the sunshine then watching the England game as well as the Bale v Scotland game lol. Sat at a table with a young couple from Glasgow, amzing how mild the language was considering the result.
jimmy pearce jimmy pearce Oct 13, 2012 23:40 Flag
thanks NBR...basically. until they got the Russian money, as we all thought, we'd stuffed them for decades!
- 1 Reply to jimmy pearce
A Yahoo! User Oct 21, 2012 19:11 Flag
Strangley enough Spurt Boy, In the days before Roman took over, Chelsea used to thrash the Spurts home and away. That's why your ground is called 3 point lane.


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Oracle Social Cloud Spring Road Show for OPN Members
By Michaela Seika-Oracle on Mar 25, 2014
The Oracle Social Relationship Management Sales Awareness Boot Camps are designed to enable partners with the selling tools required to present solutions to their clients with a basis of understanding of the technology, its features and its market preparedness.
By the end of the boot camp each student should be able to articulate several key points including (but not limited to) value proposition, strengths of the product, key markets, etc. Each student will complete the course with a level of understanding which will allow them to take and pass the Oracle Social Relationship Management Sales and/or Pre-Sales Assessment exam.
What will we cover:
- Social Sales Strategies
- Oracle SRM Value Proposition
- Key Differentiators
- Live Domonstrations
- Go to Market Playbook and How to Work with Oracle
- Oracle SRM and CX Application Integrations
Social Relationship Management Sales Awareness Boot Camps
Reserve Your Spot Today!
Mar 27 - Mar 28, 2014
Reston, VA
Apr 8 - Apr 9, 2014
Irving, TX
Apr 24 - Apr 25, 2014
San Francisco
May 12 - May 13, 2014
Colombes, France
May 14 - May 15, 2014
London, UK
Audience Format:
This training is targeted for partners. Any sales, pre-sales, practice leads, consultants, marketers, and social media and community managers are welcome
The boot camp is an instructor led hands-on workshop, available as a one and half day in-class format.
Registration Fee:
- These events are offered FREE of charge to select Oracle Partners.


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Rough space JUN 03 2011
Space photography and videography all looks pretty much the same: high contrast, lots of black backgrounds, smooth, and often sterile. Designer Chris Abbas took a bunch of photos from the Cassini Mission (to Saturn) and made them into something that is definitely not your usual NASA video.
(via stellar)


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A Delicate Balance
Organizational barriers to evidence-based management
For analytics projects to succeed, the strengths and limitations of data-driven analytics and expert decision-making must counterbalance each other. "Data mining" must give way to "data dialogues".
"The most difficult subjects can be explained to the most slow-witted man if he has not formed any idea of them already; but the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already, without a shadow of doubt, what is laid before him." — Leo Tolstoy
Over a hundred years ago, H. G. Wells stated that statistical thinking would one day be as necessary for efficient citizenship as the ability to read and write.1 Wells' prescient comment is equally true of management and organizational behavior in the age of big data and business analytics. In domains as varied as professional sports, medicine, consumer business, financial services and government operations, a consensus has rapidly developed about the power of statistical thinking to help experts make better decisions and businesses improve their operations. And a stream of best-selling books, movies and podcasts on the topic has piqued societal awareness of analytics as a catalyst for fresh thinking and change.2
However, in our many years as consultants, we have found that the realized benefits of business analytics are unevenly distributed across domains, and even among different organizations within the same domains. One might chalk this up to the fact that intelligently working with data and doing statistical analysis is hard work, involving specialist skills. Fair enough. But this turns out to be only part of the problem.
Generally speaking, data analysis is only part of an "analytics" project; and ironically it often isn't the hardest part. It is not uncommon for sophisticated technical work to end up on the cutting room floor—resulting in unrealized value—for reasons having more to do with human and organizational behavior than the finer points of data quality or statistical methodology.
In previous articles, we have discussed the ways in which business analytics is more than a story about arcane statistical algorithms, big data management and information technology.3 Certainly the newfound prominence of business analytics owes much to Moore's Law and its corollaries. But business analytics is not ultimately about technology and technique any more than architecture is about blueprints and drafting tools. Well-conceived analytics projects are directed at the central problems and processes in the domain at hand. For example, in medicine this might mean making more reliable diagnoses and triage decisions. In insurance this might mean making better underwriting, pricing or claim settlement decisions. In a human resources context this might mean making better hiring and talent management decisions.
A common challenge in such applications is achieving a realistic compromise between a current-state business or decision process and an envisioned ideal that could in theory be achieved with perfect data and the best available analytics. We have encountered many organizations that, often out of a combination of inertia, competing priorities and culture of skepticism about the effectiveness of business analytics, spend years deliberating before taking the first step toward embracing analytical methods. Others eagerly embrace the notion of analytics but treat it as an "all-or-nothing" proposition requiring data or algorithmic perfection before actions can be taken. Some organizations swing from one extreme to the other.
Of course the preferred point is somewhere between these extremes: in many business settings, analytics is best viewed as an iterative process of continued improvements and data-driven refinements of core business operations. In such settings, either extreme skepticism leading to inaction or extreme aspiration leading to analysis paralysis is suboptimal. Such extreme attitudes and approaches are often borne both of sketchy notions of how analytics works and poor communication between the technical people who analyze data and the decision-makers for whom the fruits of their efforts are intended.
The legendary statistician John Tukey memorably characterized large-scale data analysis as "the collision between statistics and computing." Similarly, if not properly planned and managed, business analytics projects can feel like a "collision between data analysis and business decision making." If they are to enjoy the benefits of analytical methods, organizations should strive to avoid collisions and promote evolutions, syntheses and collaborations among people with differing skills and perspectives.
To that end we humbly offer a taxonomy—based on our observations in the field—of what can go wrong. A connecting theme of our observations is that analytics projects are often stymied because of failures to appreciate that both data-driven analytics and expert decision making have strengths as well as limitations and that the strengths and limitations of each much be counterbalanced with those of the other. The image of "data mining" should give way to the image of "data dialogues."
An intriguing aspect of business analytics is its near-universal applicability, yet this also accounts for why it can be such a slippery topic to discuss. Analytics projects take on vastly different aspects in different contexts. For example, the authors have built credit-scoring models using tens of millions of data points as well as analyzed human resources databases containing mere hundreds of data points. Size matters, but it's not decisive. Similar comments can be made about data quality and completeness, the relative appropriateness of "supervised" versus "unsupervised" learning techniques, the relative appropriateness of experimental versus observational data, appropriate validation methodologies and so on.4 Such considerations are context-dependent and can vary in veracity and business significance in real-world settings. To bring order to the kaleidoscopic—and ever expanding—variety of applications and methodologies, a classification scheme might be helpful.
One way to classify analytics projects is by the degree to which decision making can be outsourced to computer algorithms. Some of the more prominent examples of business analytics hinge on computer algorithms that serve as decision-making "robots" whose day-to-day functioning involves a minimum of human intervention. Think of Netflix using collaborative filtering algorithms to suggest new titles based on a customer's viewing history.5 In each case an algorithmic, data-driven approach both refines and scales up a traditional mode of doing business: Savvy booksellers and video store clerks can be very adept at recommending books and movies to their loyal customers. But even Quentin Tarantino in his video store heyday could not make movie recommendations on the scale of Netflix's recommendation engines.
At the other extreme, consider an executive at a global reinsurance company recommending how much capital to set aside in reserves as a cushion for adverse events. Any such manager worth her salt will make the decision in the light of highly sophisticated analyses of past loss trends, correlations among the risks in a portfolio, and stochastic simulations of future economic conditions and other macro factors. But the decision remains solidly with the executive and is unlikely to be left up to the indications of a purely automated algorithm.
A connecting theme of our observations is that analytics projects are often stymied because of failures to appreciate that both data-driven analytics and expert decision making have strengths as well as limitations and that the strengths and limitations of each much be counterbalanced with those of the other.
The term "business analytics" is broad enough to apply equally well to each of these extreme instances. In each case, data analysis is used to guide a business decision, and the result is decisions that are—on average and in the long run (think the Law of Large Numbers)—better than those that would result from unaided judgment. In the book and movie examples, the machine learning algorithms and induction rules simply replace human decision-makers (the store clerks); in the reinsurance example, the analytical results serve as inputs to a decision that remains fully under the purview of a human decision-maker.
Our focus in this article is the broad swath of analytics applications falling at various points on the spectrum bounded by these two extremes. It is in this "middle realm" that the success of business analytics can be most surprising and sometimes downright counterintuitive.6 Medical decision making offers a good paradigm example. Here, highly trained professionals—medical doctors—regularly make decisions under uncertainty. Which of two patients arriving in full crisis at the emergency room complaining of chest pains should be admitted first? Given a positive outcome of an imperfect test, should a patient be treated for a rare disease? Should a risky operation be recommended to a patient? It is hard to imagine such decisions being turned over to a purely algorithmic process similar to the ones used to recommend movies or make targeted marketing decisions. The stakes are too high and the evidence too subtle and complex to turn over to a purely automated decision process.
Yet one could also argue that, precisely because the stakes are so high and the evidence so subtle and complex, the opposite strategy of entrusting medical decision making to the unchecked professional judgment of doctors is similarly suboptimal. In Blink, Malcolm Gladwell provides a memorable example that illustrates the point.7 Gladwell's anecdote begins in the late 1990s at the resource-strapped Cook County Hospital emergency room. (As it happens this was the very emergency room that inspired the television show E.R.) Brendan Reilly, the chairman of the hospital's Department of Medicine faced 250,000 patients visiting the Emergency Department (ED) each year. An average of 30 arriving patients per day complained of chest pains and worried they were having heart attacks. This presented ED physicians with the formidable problem of rapidly deciding which patients to send to intensive care, which to send to intermediate care, and which to send home. In a controlled experiment, Reilly found that a computer-driven decision-rule protocol was markedly more accurate than the unaided judgment of physicians. In a JAMA article summarizing his work, Reilly reported that 84 percent of the physicians he surveyed believed that the decision-rule approach improved patient care.8 Reilly himself concluded that the analytics-driven rules approach improved efficacy without compromising patient safety.
More generally, there is now considerable evidence that Computerized Decision Support Systems (CDSS) can improve both practitioner performance and patient outcomes.9, 10 And Atul Gawande has written eloquently about the power of simple checklists—which in other domains would be called business rules—to improve the delivery of medical care.11 In a New York Times op-ed, none other than Billy Beane joined the chorus of medical, political and business leaders who prescribe a data-driven, evidence-based approach to medical care analogous to the evidence-based methods he famously used to bring the Oakland A's up in the ranks.12, 13
Obviously none of this work suggests that physicians could or should be replaced with purely automated decision protocols. What it does suggest is that purely "clinical" decision making—one extreme end of our spectrum—is likely a sub-optimal model for much medical decision making. In many situations, physicians make better decisions armed with data-driven predictive models, decisionrule sets and checklists than they do relying on unaided professional judgment.
What can be applied to medical decision making can also be applied to decision problems in domains as diverse as human resources and talent management (Moneyball has become a classic example), risk management, insurance and loan underwriting, fraud detection, caseworker deployment, retail pricing and understanding the organizational drivers of employee resignations. In each of these domains—and many others—evidence-based methods have been shown to outperform the unaided judgment of trained professionals.14 We believe that, as with medical decision making, the rise of data-driven decision making does not presage the end of professional judgment in any of these fields. There will always be a need for HR managers and talent scouts to make hiring decisions; risk and insurance professionals to make risk management, underwriting and investment decisions; and caseworkers in government, business, and education to make various decisions serving citizens, customers, employees and students.
While the march of business analytics will not replace professional judgment, it can continually transform, enhance and refocus professional judgment. This is a major reason it would be a mistake to view business analytics as a technical domain beginning with data analysis and ending with computer algorithm implementation. Professional judgment enters this process at two crucial points. First, professional judgment and domain knowledge should be used to frame, prioritize and inform specific steps in the process of analyzing data to build predictive models or craft rule sets and checklists. Second, no predictive model or decision rule is complete or infallible: Human judgment is needed to decide when to use, temper or simply ignore model indications. There is no simple recipe for doing this. The process is typically a pragmatic blend of art, science and case-specific business strategy. In short, both analytical methods and the traditional decision processes they are intended to improve have strengths and weaknesses that should be pragmatically counterbalanced.15
F. Scott Fitzgerald famously wrote that "the test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and retain the ability to function." A similar comment applies to an organization's ability to execute on analytics. A prerequisite for achieving organizational buy-in of analytics is understanding, forming a strategy around, and communicating the required interplay between analytical methods and the best available domain knowledge and judgment.
The biggest challenges of executing on analytics are often found where algorithmic indications should be integrated with human professional judgment. Because of the range of personnel involved, this is an inherently organizational issue. Unsurprisingly, challenges often arise from such sources as office politics, inertia, principal/agent issues and organizational dynamics. Such generic project implementation issues often take on added force because business analytics may often be poorly or inconsistently understood by the various stakeholders within the organization.
As a result, one often encounters extreme or overly simplistic attitudes about predictive analytics. At one end of the continuum is the sort of extreme skepticism and hostility to analytical methods dramatized in such books as Moneyball and Super Crunchers.16 At the other, models are tacitly regarded as repositories of truth rather than provisional, imperfect decision aids that should be continually monitored and subjected to critical thinking. Models are either demons or angels. We believe that such extreme conceptions are at the root of many of the organizational biases that we have observed over the years.
"We tell ourselves stories in order to live." — Joan Didion
Business analytics is typically viewed as a "techy" or "geeky" subject because of its statistics and machine learning subject matter as well as the need for such IT-heavy contributions as data warehousing, systems implementation and dashboard reporting. We tend to regard business analytics in the context of what economists call "human capital." After all, decision making and decision makers—a.k.a. people—are central to all enterprises, and decades of academic research and business experience suggest that data-driven methods can help even highly trained domain experts make better decisions.17, 18
This is not just because our databases are now so deep and rich or that we now possess powerful analytical tools and techniques. It is also because we human beings are so surprisingly bad at weighing evidence, juggling probabilities and making consistent, coherent decisions in the face of uncertainty. Business analytics is therefore as much about human psychology as it is about data and algorithms.
As an example, take a moment to form a mental image about Linda. Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Now that you've formed your mental image, rank these three scenarios in order of likelihood:
- Linda is active in the feminist movement.
- Linda is a bank teller.
- Linda is a bank teller and is active in the feminist movement.
The pioneering psychologists Daniel Kahneman and Amos Tversky posed precisely this question to groups of students at several major universities. Kahneman discusses this experiment in Thinking, Fast and Slow.19 Not surprisingly, most of the students felt that being active in the feminist movement was the most likely scenario given what we know of Linda. But at each university, between 85 percent and 90 percent of them also felt that being a bank teller was the least likely of the three scenarios. In other words, they judged that Linda's being a feminist bank teller is more likely than Linda's being a bank teller. But a moment's reflection reveals that this cannot possibly be the case: Feminist bank tellers are a subset of all bank tellers! The probability of being a feminist bank teller must therefore be lower than the probability of being a bank teller.
In this example, our intuitions can lead us badly astray in a way that is as surprising as it is straightforward. Kahneman attributes phenomena such as the Linda story to a certain type of mental process that he called "Type 1." Type 1 mental processes are fairly automatic, effortless and place a premium on "associative coherence." In contrast, "Type 2" mental processes are controlled, effortful and place a premium on logical coherence. Although we fancy ourselves primarily Type 2 creatures, many of our mental operations are Type 1 in nature. And—here's the rub—Type 1 mental processes are very poor at statistical reasoning.20 This is a major—and, in business, too often neglected—reason why analytical methods are taking root in broad swaths of business, government and medicine. Models can serve as correctives for the bounded rationality and biased cognition of human decision-makers.
Ironically, the dominance of Type 1 thinking can also lead to the organizational resistance to the very analytics initiatives that can help organizations become more "Type 2" in nature. A major culprit is the so-called "overconfidence bias." So far are we from being naturally statistical thinkers and rational decision-makers that Kahneman characterizes the mind as a "machine for jumping to conclusions." He comments that "neither the quantity nor the quality of evidence counts for much in subjective confidence. The confidence that individuals have in their beliefs depends mostly on the quality of the story that they can tell about what they see, even if they see little." (Italics added.) This is why human experts' confidence in their own judgments systematically exceeds those judgments' accuracy. Kahneman calls this phenomenon "the illusion of validity." It is no wonder that the corrective power of predictive models is so counterintuitive to people making decisions in the field.
So far are we from being naturally statistical thinkers and rational decision-makers that Kahneman characterizes the mind as a "machine for jumping to conclusions." He comments that "neither the quantity nor the quality of evidence counts for much in subjective confidence."
This helps explain a phenomenon we have long noticed in our consulting work: Often it is senior leaders and decision-makers who are skeptical about the economic value of predictive models. In light of Kahneman's observations, this makes sense. After all, such individuals have had the longest time to form an "associatively coherent" body of narratives pertaining to their domains: which draftees will make the best baseball player; which student to admit; which intern to hire; which insurance risks will profit the company; which medical protocols can be cut short. Perhaps their eminence has resulted, in part, from their skill at weaving convincing narratives that impress their colleagues. Their seniority lends them an air of authority, and indeed part of their success might be attributable to their charisma and ability to convince their colleagues with their narrative accounts.21 Unfortunately, given the authority that such individuals enjoy within their organizations, their resistance can seriously hinder the progress of analytics projects.
We have witnessed situations in which a few well-positioned skeptics have wielded disproportionate influence over the fate of predictive modeling projects. Consistent with Kahneman's discussion, such people tend to disbelieve models and be most confident in the accuracy of their own judgments. In conversation and in meetings, they often emphasize a relatively small number of instances where a model makes counterintuitive predictions, and deemphasize the unproblematic majority of instances. We have seen convincing "anti-modeling" narratives wrapped around memorable cases where a model appears to make a novice error that no competent human expert would ever make. The appropriate response is to analyze such cases with the perspective that (a) models combine the information that they are presented with and (b) no model is perfect, and analyzing anomalies and outliers is a standard way to improve a model. In analytically minded organizations, this is the natural response. But in cultures where anti-model skepticism dominates, such narratives can take on a life of their own.22
Another key finding of behavioral economics is the surprising prevalence of the so-called "availability heuristic:" One's estimate of an event's likelihood is affected by how easily it comes to mind. For example, people fear perishing in an airplane accident more than perishing in an auto accident even though the former is actuarially less likely; in academic studies, people have been willing to pay more for terrorism insurance than insurance that covers multiple perils including terrorism; and people tend to estimate that words ending in "ing" are more frequent than words whose penultimate letter is "n." We have seen examples of apparent model failure lead to conclusions that the model in question is not to be trusted. In these situations, offering statistical evidence of high model accuracy and segmentation power on out-of-sample validation data is only weakly effective against such "cognitively available" stories. The irony is amusing and frustrating in equal measures: The very types of cognitive biases that the model is intended to ameliorate are themselves responsible for institutional "organ rejection" of the model.
Such problems are cultural rather than technical in nature and therefore do not lend themselves to easy answers. Achieving proper communication, unbiased assessments and organizational buy-in are often no less challenging than achieving technical excellence.
"Any sufficiently advanced technology is indistinguishable from magic." — Arthur C. Clarke
We have just discussed an organizational bias that might be called "model accuracy neglect"—the tendency to overestimate the accuracy of one's own judgments and regard predictive models with undue skepticism. It is also worthwhile to explore a set of organizational biases that tends to the opposite direction: undue deference to analytical techniques and practitioners, and lack of critical thinking in model design and execution.
George Box, one of the world's preeminent statisticians, is widely known outside the statistical community for his aphorism, "all models are wrong, but some are useful."23 It is a sign of the times that one now hears academic statisticians regularly quoted at business conferences and in the popular press. Box's motto expresses a subtle idea in a mere eight words. But perhaps this idea is too subtle. For Box's message is often distorted (as in "it's not too bad to bend the rules") in ways that lead to this second type of organizational bias.
Two themes are important. First, it is important not to lose sight of the practical context of modeling projects: The goal is not "absolute truth" of the sort sought in fields like mathematics and physics. Rather, it is improved decisions. Second, it is important to have a realistic conception of what models can and cannot do. At the opposite end of the spectrum from "model accuracy neglect" lies another type of organizational bias that might be called "magical thinking about analytics."
Business analytics practitioners are often motivated by the sheer pleasure of using mathematics and scientific reasoning to arrive at useful facts and insights. The authors remember hearing about a prominent executive of a major insurance company—an actuary by training—who was spotted reading one of Einstein's original essays on relativity theory while traveling on the corporate jet. The scientific motivation is both admirable and valuable and should be encouraged by organizations wishing to become more analytically oriented. At the same time it is important to remember that the goal of any business analytics project is not "Truth with a capital T" but converting raw data into insights, inferences or predictive models that can lead to better decisions. The goal is not "Truth" but "true enough to be useful."
This is the essence of Box's motto, one that becomes clearer in one of its less quotable versions: "Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful."24 The thought seems transparent to the point of requiring no comment. Yet in practice we see it violated frequently and in a variety of ways. Examples include:
The data perfection syndrome: Organizations often defer analytics projects until such time as an elaborate analytics data warehouse has been constructed. One often hears comments like "first we need to get our data house in order." Fair enough, but in many situations this can amount to leaving on the table millions of dollars of savings that could be realized from imperfect and provisional—yet practically effective—models built with imperfect data. A common sentiment is that one's data needs to be in excellent shape in order to begin analysis. This is typically a mistake. Just as "all models are wrong," one could also say that "all databases are incomplete." We have found that, more often than not, something useful can be gleaned even from highly imperfect data. Indeed, analyzing provisional or imperfect data can help focus the organizations' thinking about what new data elements to collect or how to improve the collection of existing data elements.25 Furthermore, incomplete data can often be augmented by publicly available or third-party data sources.
Analytics experts are humans, too. And just like the decision-makers whom models are intended to help, analytics experts can be overly confident both in their abilities and in the accuracy of their judgments. This can be exacerbated by the fact that analytics experts possess uncommon skills that many consider advanced or esoteric.
The super-model syndrome: An analogous organizational bias is failing to distinguish between a good-enough, "satisficing" model and a theoretically ideal model. As with holding out for perfect data, significant benefits are often sacrificed by engaging in a snark hunt for model perfection or failing to account for the opportunity cost of striving for greater degrees of accuracy. We believe that this organizational tendency results at least in part from a "magical" view of models as repositories of truth rather than inherently imperfect but (in varying degrees) useful decision tools.
Outsourced critical thinking: A related organizational bias is a naïve belief that "the answers are all in the data" or "the quants have figured this out for us." These are perhaps not bad guiding principles in data-rich, low-risk situations such as recommending books and movies. But in cases where the data are messy, incomplete, ambiguous and/or of limited quantity, considerable institutional knowledge, domain expertise and common sense is needed to effectively make sense of it. Popular phrases such as "data mining" might be partly to blame here. Mining for nuggets of gold is a helpful metaphorical image for a certain kind of algorithmic-powered knowledge discovery. But real-world data analysis more often resembles a dialogue between indications from data and the active hypothesis formation and critical thinking of the data analyst. Furthermore, there is no way to guarantee that the people within an organization best equipped to analyze the data (analysts) are also in the best position to interpret the results. We have been privy to a number of predictive modeling projects that ended badly because the business people outsourced necessary critical thinking entirely to analytics personnel who, while skilled, did not have the appropriate perspective to properly design the analysis and interpret the results. In more than one case, we have witnessed the results of analysts who actually built models to predict the wrong quantity—a decision that should have been discussed and signed off on near the beginning of the project!
Over-confident analysts: Analytics experts are humans, too.26 And just like the decision-makers whom models are intended to help, analytics experts can be overly confident both in their abilities and in the accuracy of their judgments. This can be exacerbated by the fact that analytics experts possess uncommon skills that many consider advanced or esoteric. However, specialist quantitative skills are not the same thing as critical thinking ability. To take one example, we encounter arguments from authority with unfortunate regularity. This has manifested itself (for example) in analysts stonewalling or rejecting useful methods that do not conform to textbook assumptions; electing to predict an easy (such as a binary) quantity that conforms to textbook assumptions rather than attempting to predict a more complex (for example highly skewed) quantity that would yield more powerful results; or mistaking statistical significance for business significance.27 More generally, we have nearly all felt the ramifications of an outright modeling blunder. As was widely reported in the wake of the 2008 market downturn, at least one rating agency used a model of home price changes that could not accept negative numbers.28 In such cases the damage can be mitigated or avoided by injecting critical thinking, checks and balances, and communication among people with a variety of perspectives into the process. Analytics should be viewed neither as an "ivory tower" nor a "back room" exercise.
Glamorous models: Here another George Box quote is apropos: "Statisticians, like artists, have the bad habit of falling in love with their models." A common manifestation of this tendency is continuing to refine an analysis or model past the point of diminishing returns. A less obvious manifestation is failing to appreciate—or failing to communicate—a model's limitations, assumptions or inherent risks. Once again, a dramatic example came to light after the market downturn. The statistician and Wall Street quant David X. Li, at one time called "the world's most influential actuar," became famous for a model that greatly simplified the complex relationships among the various securities underlying collateralized debt obligations.29 Li's model seemingly offered its users the ability to price complex securities that had been considered too difficult to price. Unfortunately, the model was too simple to support its widespread use. Box's aphorism notwithstanding, it was not Li himself who fell in love with his model; it was the larger derivatives pricing world. Well before the 2008 crash, Li both articulated the limitations of his model and nicely captured a type of organizational bias in the adoption of models: "The most dangerous part … is when people believe everything coming out of [the model]."30
Li's comment speaks to the dangers of "magical thinking" about analytics and models: the notion that models are repositories of truth rather than inherently provisional and imperfect—but useful—tools for guiding actions.
In an interview, economist and Financial Times columnist John Kay provided a clear statement of this position, one that is perhaps less open to misinterpretation than Box's concise motto. Kay was asked why investment models, built by people with quantitative PhDs from elite universities, appeared to fail. Kay replied:
"Put simply, people made the mistake of believing the model. The people who built them—the mathematics PhDs—didn't know very much about the world. The people who knew about the world didn't understand the mathematics. Both groups had inappropriate confidence in the value of these models. They aren't useless—but models can only illuminate the world, never be a substitute for judgment."31
Organizations wishing to be first-rate analytical competitors should therefore cultivate the ability to function without losing sight of two opposing ideas about business analytics. On the one hand, in domain after domain, many models have been shown to be effective in helping human specialists make decisions more consistently, accurately and economically. Models are useful. On the other hand, models in these domains often tend to be not repositories of "truth" but rather inherently provisional decision tools that benefit from continual improvement. The goal is therefore not so much to choose between specialists and equations but rather to set up a virtuous cycle whereby one continually works to improve the functioning of the other.
While there is no simple recipe for achieving this, promoting dialogue between groups with different perspectives and skills is a good way to begin. Modelers can do more effective work when they are in continuous dialog with the decision-makers for whom their work is intended. Not incidentally, this also helps reduce the chances of nasty downstream surprises, expensive implementation snags and unmet expectations that manifest themselves only at the close of a project. Conversely, such dialogue can help achieve organizational buy-in of analytics in an organic, incremental way rather than via a collision between data analysis and traditional judgment-driven modes of decision making. In many organizations, promoting such communication is as important an executive function as articulating a strategic vision for analytics in the first place.
Above all such dialogue can help the organization avoid the extremes of skepticism-induced inaction and delay resulting from pursuing unnecessary degrees of perfection. Both extremes are expensive places to reside.
EndnotesView all endnotes
- Wells actually wrote, "The time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex world wide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write." H. G. Wells, Mankind in the Making (1904). Wells is commonly paraphrased as having written "statistical thinking."
- A recent example: Michael Lewis' book Moneyball, a book which we view as popularizing the concept of "actuarial versus clinical judgment," has recently been turned into a major Brad Pitt movie. (A landmark academic article in this field is "Clinical versus Actuarial Judgment" by R.M. Dawes, D. Faust, and P.E. Meehl, Science, 31 March 1989 <http://www.sciencemag.org/content/243/4899/1668> ). A second example is a journalist at Slate magazine taking an online Stanford University Machine Learning class and blogging about the experience. See "Blogging the Stanford Machine Learning Class" by Chris Wilson, Slate, October 18, 2011. <http://www.slate.com/articles/technology/future_tense/features/2011/learning_machine/
- "Irrational Expectations" Deloitte Review Issue 4, 2009 and "Beyond the Numbers" Deloitte Review Issue 8, 2011
- Supervised learning involves predicting or explaining a well-defined target variable. Regression analysis is a common example of supervised learning. Unsupervised learning involves finding "interesting" patterns, associations, or groupings in a multidimensional database. Consumer segmentation is an example of unsupervised learning.
- See for example, "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model," by Yehuda Koren: <http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf> Koren is a member of the team that won the Netflix Grand Prize.
- One of our favorite counterintuitive examples was used to open Ian Ayres' book Super Crunchers: the Princeton Economist Orley Aschenfelter has successfully used simple regression models to predict the future value of fine Bordeaux vintages from basic information about growing season temperatures and rainfall. The initial reaction of eminent wine critics was one of dismay and disbelief. This is understandable because one would intuitively think that judging wine quality would be an example where objective, statistical analysis is helpless against the nuanced perceptions of a sophisticated palate. A rich, oakey blend of data, scholarship, and tasting reports is available at Aschenfeter's website: <http://www.liquidasset.com/>
- See "A Crisis in the ER" in Malcolm Gladwell's book, Blink: The Power of Thinking without Thinking (Little, Brown and Company, 2005).
- "Impact of a Clinical Decision Rule on Hospital Triage of Patients with Suspected Acute Cardiac Ischemia in the Emergency Department," JAMA, July 17 2002. <http://jama.ama-assn.org/content/288/3/342.full.pdf>
- See "Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes," JAMA, March 9, 2005 <http://jama.ama-assn.org/content/293/10/1223.short>
- See "Improving Clinical Practice Using Clinical Decision Support Systems: a Systematic Review of Trials to Identify Features Critical to Success," BMJ, March 14, 2005 <http://www.bmj.com/content/330/7494/765.full.pdf>
- See "The Checklist" by Atul Gawande in the December 10, 2007 issue of The New Yorker <http://www.newyorker.com/reporting/2007/12/10/071210fa_fact_gawande> or The Checklist Manifesto by Atul Gawande (2011 Picador).
- See "How to Take American Health Care From Worst to First," Billy Beane, Newt Gingrich, and John Kerry, The New York Times, October 24, 2008 <http://www.nytimes.com/2008/10/24/opinion/24beane.html>
- Michael Lewis, Moneyball: the Art of Winning an Unfair Game (W. W. Norton & Company, 2003).
- The concept of "evidence-based management" is by no means original with us. A good resource is Hard Facts, Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management by the Stanford professors Jeffrey Pfeffer and Robert I. Sutton (HBS Press, 2006). Also a good source is Pfeffer's and Sutton's website <http://www.evidence-basedmanagement.com>
- In his book Super Crunchers, Ian Ayres elegantly captures this thought: "The rise of statistical thinking does not mean the end of intuition or expertise. Rather, [it] underscores how intuition will be reinvented to coexist with statistical thinking. Increasingly, decision makers will switch back and forth between their intuitions and data-based decision making. Their intuitions will guide them to ask new questions of the data that nonintuitive number crunchers would miss. And databases will increasingly allow decision makers to test their intuitions—not just once, but on an ongoing basis … while there is now great conflict between dyed-in-the-wool intuitivists and the new breed of number crunchers, the future is likely to show that these tools are complements rather than substitutes. Each form of decision making can pragmatically counterbalance the greatest weaknesses of the other." (page 195)
- Daniel Kahnemal also discusses this issue in the chapter "The Hostility to Algorithms" in Thinking, Fast and Slow.
- In Administrative Behavior: A Study of Decision-Making Behavior in Administrative Organizations (4th ed), page xi, the polymathic scholar and proto-behavioral economist Herbert Simon writes: "Decision-making is at the heart of administration."
- The University of Minnesota psychologist Paul Meehl was a pioneering figure in the academic study of what has come to be called "actuarial versus clinical prediction." Towards the end of his career, Meehl commented, "There is no controversy in social science which shows such a large body of quantitatively diverse studies coming out so uniformly in the same direction as this one. When you are pushing over 100 investigations, predicting everything from the outcome of football games to the diagnosis of liver disease, and when you can hardly come up with half a dozen studies showing even a weak tendency in favor of the clinician, it is time to draw a practical conclusion." — "Causes and Effects of My Disturbing Little Book," Journal of Personality Assessment 50, pp. 370–375. In Thinking, Fast and Slow, (Chapter 21—"Intuitions vs. Formulas") Daniel Kahneman reports that "Meehl … was one my heros from the time I read his Clinical vs. Statistical Prediction: a Theoretical Analysis and a Review of the Evidence."
- See chapter 15, "Linda: Less is More" in Thinking, Fast and Slow by Daniel Kahneman, Farrar, Straus, Giroux 2011.
- In a recent edge.org master class, Kahneman reminisced that his seminal research in cognitive heuristics and biases was in part motivated by his experience teaching a statistics class. He found the material he was teaching very unintuitive and began to wonder whether this was due to a fact of human psychology that humans are not "good intuitive statisticians." See "The Marvels and Flaws of Intuitive Thinking" by Daniel Kahneman at <edge.org: http://edge.org/conversation/the-marvels-and-flaws-of-intuitive-thinking>
- Consistent with this hypothesis is the work of the University of Pennsylvania psychologist Philip E. Tetlock. In his book Expert Political Judgment: How Good is it? Tetlock discussed a study of many thousands of predictions made by experts in a variety of fields. Tetlock found that the experts performed little better than random chance, and worse than statistical algorithms. Furthermore, the more prominent experts fared worse than their less celebrated counterparts. It is likely that some of the high-profile experts' success is due to their overconfidence as well as the narrative appeal of their forecasts, rather than to the accuracy of their predictions. Tetlock wrote that, "there is no reason for supposing that contributors to top journals—distinguished political scientists, area study specialists, economists, and so on—are any better than journalists or attentive readers of the New York Times in 'reading' emerging situations." For an informative review of Tetlock's book, see "Everybody's an Expert" by Louis Menand in the December 5, 2005 New Yorker. <http://www.newyorker.com/archive/2005/12/05/051205crbo_books1>
- This is an example of a phenomenon that Timur Kuran and Cass Sunstein call the availability cascade: a collective believe formation process in which a perception or attitude becomes steadily more plausible as it becomes more prominent in a group's discourse. Kuran, Timur and Sunstein, Cass R. , "Availability Cascades and Risk Regulation," Stanford Law Review, 51 (April 1999): 683–768
- Empirical Model-Building and Response Surfaces (1987), by George EP Box and Norman R. Draper
- Ibid.
- For example, one of us had a recent conversation with an executive at a financial services company who had spent years overseeing the development of a large analytical data warehouse without having a clear idea of what the data would be used for.
- Although sometimes we wonder. One of us made the mistake of trying Daniel Kahneman's "Linda" experiment on a group of senior actuarial science majors at The University of Wisconsin. They got it right with hardly a moment's thought.
- Deirdre McCloskey wrote about this issue in her book The Cult of Statistical Significance.
- See, for example "The End" by Michael Lewis in Condé Nast Portfolio.com, December 2008 <http://www.portfolio.com/news-markets/nationalnews/portfolio/2008/11/11/The-End-of-Wall-Streets-Boom>
- See "The Formula that Felled Wall Street," Financial Times, April 24, 2009 <http://www.ft.com/intl/cms/s/2/912d85e8-2d75-11de-9eba-00144feabdc0.html#axzz1cspGosg2>
- See "Slices of Risk" by Mark Whitehouse, The Wall Street Journal, September 12, 2005. <http://math.bu.edu/people/murad/MarkWhitehouseSlicesofRisk.txt>
- See "The Long and the Short of It: John Kay interview and review," Financial Times, February 1, 2009. <http://globalcomment.com/2009/the-long-and-the-short-of-it-john-kay-interview-and-review/>


text:
What are the symptoms of ADHD?
The person with ADHD who is inattentive will have 6 or more of the following symptoms:
- Has difficulty following instructions
- Has difficulty keeping attention on work or play activities at school, work and home
- Loses things needed for activities at school, work and home
- Appears not to listen
- Doesn't pay close attention to details
- Seems disorganized
- Has trouble with tasks that require planning ahead
- Forgets things
- Is easily distracted
The person with ADHD who is hyperactive/impulsive will have at least 6 of the following symptoms:
- Fidgety
- Runs or climbs inappropriately
- Can't play quietly
- Blurts out answers
- Interrupts people
- Can't stay in seat
- Talks too much
- Is always on the go
- Has trouble waiting his or her turn
People who have ADHD have symptoms for at least 6 months.
Next: Causes & Risk Factors
Adult ADHD: Evaluation and Treatment in Family Medicine by HR Searight, Ph.D., JM Burke, Pharm.D. and F Rottnek, M.D. (American Family Physician November 01, 2000, http://www.aafp.org/afp/20001101/2077.html)
Written by familydoctor.org editorial staff
Reviewed/Updated: 02/14
Created: 04/97


text:
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text:
Eager eyes scanned the pieces of paper hanging on the windows of the front entrance of Pattengill Middle School, the sheets that held each student's fate for the 2013-2014 academic year.
Some children celebrated as they discovered who their teacher and classmates would be. Others hugged their friends as they approached the building, separated by three months of summer.
"Everyone's excited," said Dominic Carbone, a local business owner who volunteered to greet kids as they arrived Tuesday morning. "It's like the first day of a sporting season almost. They're all excited for the new year. They're all straight-A students and they all feel great about themselves."
Some parents joined their students in rejoicing the start of school, glad their kids could begin learning again and thankful for more time to run some errands. Others were more nervous as their children transitioned to a new school.
Superintendent Yvonne Caamul Canul greeted children in the front of the building before giving a brief address to the entire school in the gymnasium.
"You always have to keep your mind clear about why you're doing this," she said. "It's for the kids and for the families in Lansing. And if I stay in my office, I miss that opportunity to be energized by everybody in here."


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Mevagissey Cottages
Just show me Mevagissey Cottages
For a truly magical Cornish holiday, try one of our Mevagissey holiday cottages located in and around this enchanting, atmospheric fishing village.
Down at the quayside watch local fishermen tend their nets and daily catch - lobster, plaice, skate and sole - to be featured later as stars on the specials' boards of Mevagissey's pubs and restaurants, often with alfresco dining areas for those balmy summer evenings. Discover more about their centuries-old trade and join them on a sea fishing trip.
Wander the town's attractive alleyways lined with charming galleries, shops and craft workshops - originally haunts of sea-faring smugglers. The beaches of Polstreath and Portmellon are a short walk away,while neighbouring Gorran Haven offers two superb village beaches ideal for family days out. Walks not-to-miss include Polkirt Hill and the coastal path.
Book a Mevagissey cottage in summer and look out for Feast Week, when the town comes alive with music, dance and carnival parades. Or try a festive break - the town boasts a wonderful display of Christmas lights and everyone dresses up at New Year.
Award-winning attractions near your Mevagissey cottage
Top of the list comes the Eden Project, a fascinating eco attraction known as the '8th wonder of the world' and offering an array of attractions for every age. Next, the Lost Gardens of Heligan - a mysterious estate full of rare features like giant rhubarb plants and Alpine ravines.
Back in Mevagissey, there's the Museum of Model Railways, the Old Lifeboat House Aquarium and the Folk Museum. If you need anything else, pop into the local tourist office.


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Top Ohio counties for injected drilling waste dumping: 1. Portage, 2. Washington and 5. Noble
July 15, 2013
AKRON— Booming energy production in shale formations has made a northeast Ohio county the top location in the state for underground injection of drilling wastes....
Chestor05 - One ironic point about it is, the good people of the city of Athens, Ohio were considering legislation support to prevent hydraulic fracture well activity within 50 miles of the city. Although brine injection disposal is not exactly the same issue, I'd say they are close to being a day late and a dollar short. SORRY, ATHENS!
1 Agrees | 0 Disagrees | Report Abuse »
Whatever your political persuasion, one thing is for sure, this article will not help property value. Obama or Romney your pick - they'll be going to next door to Athens County where they aren't as worried about the water they drink - whether it is a perceived problem or real.
0 Agrees | 0 Disagrees | Report Abuse »
The people of Eastern Athens County should be aware of the 14 injection wells recently drilled on the former Beha Farm property, across the county road from the State Roadside Rest at Torch, Ohio.
My suspicion is; bring waste in from out of state by river barge, unload it a New Matamoras, truck it to Torch and stick it in the ground.
I wonder why the people don't want it in the ground in the state from which it originated? (SNARK!)
2 Agrees | 0 Disagrees | Report Abuse »
Quoting web sites, news media and others on this subject doesn't help. A open forum with knowledgeable people from both sides is the only way to understand this. Armchair knowledge tends to be biased.
4 Agrees | 3 Disagrees | Report Abuse »
Asbestos was once touted as the best thing coming down the pike in insulation, how do you like it now? Honorable profits are great, profit now, let the govt. pay later, just like the brown site near route 7, are not honorable.....
10 Agrees | 3 Disagrees | Report Abuse »
Dumping On January 31, 2013, the company Hard Rock Excavating was found dumping an estimated 40,000 to 50,000 gallons of fracking waste into a storm drain, which eventually emptied into the Mahoning River. The Ohio Environmental Protection Agency later found documents showing that Ben W. Lupo, a partner in several companies headquartered at the site, and owner of both D&L Energy and Hard Rock, instructed an employee to dump the wastewater down the drain.
8 Agrees | 4 Disagrees | Report Abuse »
Get this anti American,anti coal,anti energy,anti christian,anti contitutional pres out of the W/house and get a REAL man or woman who LIKES the USA to run the show for a while.Not a union organizer who has never worked a day inhis life
7 Agrees | 7 Disagrees | Report Abuse »
Gasland didnt even have anything to do with disposal wells anyway.Fracking and disposal are 2 diff subjects
6 Agrees | 3 Disagrees | Report Abuse »
The Utica and Marc arent even 1% drilled in this state,By the time it does fire up,there wont be dirty water to dispose of.Norweigens(bad spell) are taking care of a economically feasible method of cleaning the water as we speak and it works.
5 Agrees | 8 Disagrees | Report Abuse »
And then the Gasland hoax..he had a propane tank hooked to his tap.Also,methane has been coming to the surface without fracking for ions before man even hit the earth.Those springs were able to be lit because of shallow meth deposits bubbling to the top as they ALWAYS have.Im not saying that the disposal wells are safe.They are 9,000 feet deeper than the water table with 5000 feet of solid limestone (Big Lime) between the water table and the TD on the well.
4 Agrees | 9 Disagrees | Report Abuse »
Josg,Ask and the rest of the regulars..I will direct my answer towards you folks...Flintknapper..I think I MIGHT know her identity and refuse to converse with her.The tree huggers are 100% responsible for this mess.We WOULDNT be drilling either of these formations(Utica,Marc) if we COULD drill offshore.But this so called president felt it was more important that foreign countries benefit the offshore wells and fields and LENT THEM US TAX $$ TO FIND IT while we were withheld from the race.ANWAR in Alaska was set aside with 1 reason and goal in mind..to drill for oil..but the tree huggers stopped it as well.They dont care where it comes from,they dont like energy except when THEY want it to run around in their PRIUS.
6 Agrees | 10 Disagrees | Report Abuse »
The oil/gas drillers will keep dumping in Ohio as long as John Kasich is govern. He has sold his soul to them cheap it only cost the lives of Ohioans.
6 Agrees | 4 Disagrees | Report Abuse »
So here's my question - most of us understand the concern, recognize the dangers, and in one case is apparently leaving the county (and taking his/her taxes with them), so what do we do about it? It's our county, right? Why are we here posting anonymously, lamenting our future land values, drinking water, and rec. lands? So what (and I'm not being rhetorical, I'm actually asking) can we do BEFORE it's a lost cause, BEFORE there's no point left, BEFORE we become an example of the problem others point to?
9 Agrees | 1 Disagrees | Report Abuse »
If it's so safe, why are we having earthquakes? - And how can we not say that a quake couldn't break into the aquifer system?
10 Agrees | 3 Disagrees | Report Abuse »
Cleaning the fracking water and making it safe for the eco system--including the people who live here--is expensive. These companies don't want anything cutting into their profit margins--and to heck with those of us who live here.
The executives from the big oil companies don't live here, don't raise their families here and don't care what happens here. We're nothing more than their toilet.
12 Agrees | 1 Disagrees | Report Abuse »
The more I read on this subject it's seems about as safe as a spoonful of plutonium for breakfast...
8 Agrees | 2 Disagrees | Report Abuse »
any of the chemicals used in the fracking process are proven toxins. These include benzene, ethylbenzene, toluene, xylene, naphthalene, polycyclic aromatic hydrocarbons, methanol, formaldehyde, ethylene glycol, glycol ethers, hydrochloric acid, sodium hydroxide, and others, which are hazardous if inhaled, ingested, or contacted by the skin and are considered caustic, carcinogenic, mutagenic, and teratogenic. Of the hundreds of chemicals tested by Endocrine Disruption Exchange, they report that 93% of them affect health and 43% are endocrine disruptors. Endocrine disruptors are man-made chemicals that, when absorbed into the body, mimic hormones or block hormones and disrupt the body's normal function. They have been linked to infertility, ADHD, autism, diabetes, thyroid disorders. Even childhood and adult cancers have been found to be linked to fetal exposure to endocrine disruptors.
11 Agrees | 1 Disagrees | Report Abuse »
@Devil, for starters...
Naturally occurring radioactive materials, known by the acronym NORM, are common in gas drilling waste. Radium, a potent carcinogen, is among the most dangerous of these metals because it gives off radon gas and takes 1,600 years to decay.
Just do a search on "fracking wastewater"..
9 Agrees | 2 Disagrees | Report Abuse »
Whenever this starts killing people, wil the politicians and companies who thought this is such a good idea be charged with criminal negligence? And, be held responsible for damages and clean up? Or, will the tax payers have to pay for it in this unending trend of corporate welfare and absence of accounatibility in the name of the almight dollar in the United States?
12 Agrees | 1 Disagrees | Report Abuse »
DevilsAdvocate , the information you seek is available from the Ohio EPA, go to their web site to learn about all 3 types of injection wells that are used in Ohio.
2 Agrees | 0 Disagrees | Report Abuse »
This is one of the prime reasons we are looking at property outside the county. (Bradford, Pa, anyone? Damascus, etc) Talk to these people - I have to a couple at least - or read their testimonies. Land value - kapoot, fishing and game? gone. And we're next on the list. "Welcome to Washington County, Ohio - fouling ourselves so other states can stay clean"
14 Agrees | 2 Disagrees | Report Abuse »
Screw the "federal commerce protections", Ohio should quit accepting hazardous waste from other states.
Eventually, such wastes will affect our children's health.
14 Agrees | 1 Disagrees | Report Abuse »
The header is frightening, but nowhere does it define, "waste". What is the waste being deposited deep under the surface? And, what is "deep"?
Anyone out there with answers? If so, please share with the blogosphere & the paper.
4 Agrees | 1 Disagrees | Report Abuse »
Federal commerce protections forbid one state from imposing tariffs or bans on legally shipped commodities from other states.
Since when is toxic fracking waste a commodity??...
9 Agrees | 1 Disagrees | Report Abuse »
Ohio is already home to many hundreds of millions of gallons of fracking wastewater. "State records show that 12.2 million barrels of fracking waste and brine were injected in the first half of 2012, 56 percent of which came from Pennsylvania and West Virginia," There are 179 disposal wells in Ohio, many more than in neighboring states.
So not much drilling here except for Disposal Wells...no wonder we don't hear much about Jobs, we're just a dumping place for everyone else...
11 Agrees | 2 Disagrees | Report Abuse »
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text:
- Aug 18:
- Reports that Jahi McMath is coming home are false
- Jun 18:
- Jahi McMath being kept at New Jersey hospital
- Jun 12:
- Jahi McMath getting diploma for eighth grade, family says
- Mar 13:
- Jahi McMath: Family calls state report 'B.S.'; new medical record details emerge
- Mar 12:
- Jahi McMath: State releases report on Children's Hospital Oakland's handling of patients
- Feb 20:
- Jahi McMath: Complete text of letter from brain-dead girl's mother
- Feb 19:
- Jahi McMath 'much better,' her mother says
- Feb 1:
- Jahi McMath: Is it safe to have tonsil surgery at Children's Hospital Oakland?
- Jan 25:
- Jahi McMath: five similar brain death legal cases
- Jahi McMath: Could her case change how California determines death?
- Jan 9:
- Jahi McMath: Medical experts say organ failure inevitable
- Jan 8:
- Jahi McMath: Girl given breathing, feeding tubes, attorney says
- Jan 7:
- Jahi McMath: Streetfighting lawyer takes heat, death threats for brain-dead Oakland girl's family
- Jan 6:
- Jahi McMath: Family says brain-dead teen's body may be too deteriorated to save
- Document: Medical analysis of Jahi McMath's deteriorating condition
- Jahi McMath: Brain-dead girl moved to undisclosed care facility
- Jan 5:
- Jahi McMath: Brain-dead teen's family moves her from Children's Hospital Oakland
- Jahi McMath: Timeline of events in case of brain-dead Oakland teen
- Jan 3:
- Jahi McMath: Mom can remove brain-dead daughter from hospital, judge rules
- Dec 31:
- Jahi McMath: Terri Schiavo group secretly leading transfer efforts
- Jahi McMath: Hospital fights in court to remove brain-dead girl from ventilator
- Dec 29:
- Jahi McMath: Statement of Children's Hospital Oakland
- Jahi McMath: Mom and lawyer say only remaining option for brain-dead girl is a New York care facility
- Dec 28:
- Jahi McMath: Family, attorney release letter addressing critics
- Jahi McMath: Family trying to raise money to get 13-year-old airlifted out of state
- Dec 27:
- Jahi McMath: Children's Hospital Oakland agrees to release brain-dead girl to long-term care
- Dec 26:
- Jahi McMath: 2 years ago, a girl wound up severely brain damaged following similar surgery
- Herhold: At bottom, the Jahi McMath story is a tale of a mother's guilt
- Dec 24:
- Jahi McMath: A mother's undeniable love is lost in divisive battle
- Jahi McMath: Judge denies petition to keep girl on ventilator past Dec. 30
- Dec 21:
- Jahi, her mom and 13 days at Children's Hospital Oakland
- Jahi McMath: Medicine's ability to keep a heart beating complicates how death is perceived
- Oakland: Emotional letter from Jahi McMath's mom to keep daughter 'warm'
- Judge grants restraining order keeping brain dead Oakland girl on ventilator through Monday
- Dec 20:
- Family of Oakland girl on ventilator furious after meeting with hospital officials
- Dec 18:
- Jahi McMath prayer vigil: 'God knows we want a miracle'
- Dec 16:
- Oakland: Girl declared brain dead after tonsil surgery may be taken off life support Tuesday
- Oakland girl, 13, declared brain-dead after tonsil surgery
Three weeks after Jahi McMath was taken out of Children's Hospital Oakland, a video claiming to show the girl's feet and toes responding to sensation has turned up on the Internet.
The video, posted on the "Keep Jahi McMath on life support" Facebook page on Sunday, showed a person's hand rubbing ice gently across the feet of a girl said to be Jahi, the Oakland teen who developed complications after tonsil, nose and throat surgery for sleep apnea and was declared brain-dead.
On Monday morning, the video had been removed from Facebook without explanation, and the link disabled. However, the video still appeared on YouTube.
A female voice also could be heard on the video. In it, the voice points out the movement in the left foot and right big toe when they come into contact with ice.
"I don't understand how a brain-dead person can do this," the woman says.
However, Dr. Neal E. Slatkin, a neurologist and chief medical officer at San Jose's Hospice of the Valley, said spontaneous movements aren't rare in brain-dead patients and can be seen in 50 percent or more of such patients.
The movement, according to Slatkin, could be spinally mediated reflexes or due to irritable nerves or muscle membranes reacting to the application of cold.
The movements are not an indication that she is alive, he said.
"She's brain-dead. She has no thought. She has no ability to control or interact with anything in her environment," Slatkin said. "She's completely dependent on machines and forever will be."
Jahi's whereabouts since being taken by ambulance from Children's Hospital Oakland on Jan. 5 have not been revealed.
Follow Rick Hurd at Twitter.com/3rdERH. Follow David DeBolt at Twitter.com/3rdERH.