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Scientists who engage with society perform better academically
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1 Science and Public Policy, (7), August 8, pages 7 DOI:./8X9; Scientists who engage with society perform better academically Pablo Jensen, Jean-Baptiste Rouquier, Pablo Kreimer and Yves Croissant Most scientific institutions acknowledge the importance of opening the so-called ivory tower of academic research through popularization, industrial collaboration or teaching. However, little is known about the actual openness of scientific institutions and how their proclaimed priorities translate into concrete measures. This paper gives an idea of some actual practices by studying three key points: the proportion of researchers who are active in wider dissemination, the academic productivity of these scientists, and the institutional recognition of their wider dissemination activities in terms of their careers. We analyze extensive data about the academic production, career recognition and teaching or public/industrial outreach of several thousand of scientists, from many disciplines, from France s Centre National de la Recherche Scientifique. We find that, contrary to what is often suggested, scientists active in wider dissemination are also more active academically. However, their dissemination activities have almost no impact (positive or negative) on their careers. R esearchers and academic institutions seem to have admitted the importance of establishing strong ties between science and society. In the UK Martin Rees, president of the Royal Society, has pointed out that Researchers need to engage more fully with the public. The Royal Society recognizes this, and is keen to ensure that such engagement is helpful and effective. A survey carried out by the Royal Society found that Most researchers have highlighted that social and ethical implications exist in their research, agree that the public Pablo Jensen (corresponding author; is at the Institut des Systèmes Complexes Rhône-Alpes (IXXI), rue du Vercors, 697 Lyon, France (affiliation ), and Laboratoire de Physique, Ecole Normale Supérieure de Lyon - CNRS, 6 allée d'italie, 697 Lyon, France (affiliation ) and, with Yves Croissant, Laboratoire d'economie des Transports, Université Lyon - CNRS, av Berthelot, 697 Lyon, France (affiliation ). Jean-Baptiste Rouquier is at affiliation and Laboratoire d'informatique du parallélisme, Ecole Normale Supérieure de Lyon - CNRS, 6 allée d'italie, 697 Lyon, France. Pablo Kreimer is at the Instituto de Estudios de la Ciencia, Solis 67, C- 78 AAU Ciudad de Buenos Aires, Argentina. The assistance of the Human Resources Management of the CNRS (especially Yavuz Demir, Françoise Godefroy, Arnaud Pes and Florence Piaud) is gratefully acknowledged. needs to know about them, and believe that researchers themselves have a duty, as well as a primary responsibility, for communicating their research and its implications to the non-specialist public (Royal Society, 6). In the Multi-year action plan, the document supposed to steer its long-term policy, France s CNRS (Centre National de la Recherche Scientifique) declared that one of its six top priorities is to transfer research results to industries and another is to strengthen the relations between science and society (CNRS, ). In February 7, CNRS organized an official workshop on Science and society in transformation, which was attended by many CNRS officials (Allix, 7). This attitude seems to be shared by the majority of researchers: in her study on the attitudes of researchers towards popularization, Suzanne de Cheveigné (7) concluded: All interviewed researchers unanimously declared: popularization is now a key and unavoidable component of research work. Motivations provided by the researchers are numerous: the yearning to inform the public, to make one s field of research better known and encourage students to take up science, or the need to account to civil society for the use of funds provided to laboratories. Science and Public Policy August 8-7/8/77- US$8. Beech Tree Publishing 8 7
2 Pablo Jensen is a physicist working at the Ecole Normale Supérieure de Lyon. He has been interested in popularization both from an effective practice (creation of scientific cafés, publication of Des atomes dans mon café crème, Seuil (), a popularization book giving a realistic vision of condensed matter physics) and by analyzing scientists practices for the CNRS. He is now the Director of the Complex Systems Institute IXXI ( Jean-Baptiste Rouquier is a PhD candidate at Ecole Normale Supérieure in Lyon and a former student of that school. His research interests include: cellular automata as a model of complex systems in fundamental research, and perturbations of complex systems. He is also interested in networks as a modelling tool (social graphs, the web, citations and coauthoring networks) and is part of the netwist.com company. Pablo Kreimer is a sociologist working at CONICET and the Universidad de Quilmes, Argentina. He obtained his PhD at the Conservatoire National des Arts et Métiers, Paris, in 997. He is the editor of REDES, the Latin American Review of Social Studies of Science and Technology. Yves Croissant is Professor of Economics at the University Lumière Lyon, France. His research interests include: micro-econometrics and public economics. The reality in the field is generally aloof from these generous ideas. For example, in the CNRS application form for candidates to the Directeur de Recherche (senior scientist) grade, a mere nine lines are provided for summarizing years of research dissemination. Likewise, the Royal Society survey concluded that, for most scientists, research is the only game in town, and popularization has to be done after one is through with real work. The purpose of this paper is to obtain an empirical picture of dissemination practices in France s CNRS. Although its prime responsibility is research done in France, the CNRS website claims that it is Europe s largest funder of research. We have presented (Jensen and Croissant, 7) a statistical view of CNRS scientists involved in popularization. Here, we also include data on teaching (teaching is not a duty of CNRS researchers) and industrial collaborations. Moreover, we correlate these data with scientists academic activity, as quantified by bibliometric records. We are therefore able, perhaps for the first time, to help answer two important questions about scientists active in dissemination beyond the usual professional publications and conference or workshop presentations: are they bad scientists as some scientists have suggested (Royal Society, 6)? Do they receive any institutional recognition in terms of their careers? We aim to answer these questions by analyzing extensive data about the academic production, career recognition and teaching or public/industrial outreach of several thousand CNRS scientists from many disciplines. Methodology Thanks to the CNRS Human Resources databases, we have gathered data on the wider dissemination activities (public outreach, industrial collaborations and teaching) of the CNRS scientists over a three-year period ( 6). It should be noted that this data has been provided by the scientists themselves in their individual annual reports (Compte Rendus Annuels des Chercheurs). This annual report is not judged to be very important for their career, serious evaluations only take place when scientists are candidates for more senior positions. However, filling out the report is mandatory and most researchers (over 9% each year) do submit it on time. Many reasons could lead to some underestimation of the amount of activities declared in these reports, including fear of misperception of popularization activities by committees, laziness in faithfully reporting these minor activities etc. Inversely, lack of control over these items could favor some overreporting of dissemination activities, although this is not likely since they have almost no perceived impact on one s career. Hence, we could anticipate some underestimation of wider dissemination activities in the figures below. Popularization activities include: public or school conferences, interviews in newspapers, collaboration with associations, and similar activities. Clearly, there is no entirely satisfactory definition of popularization. As Hilgartner (99) has convincingly shown, there is a continuous gradation going from the technical literature to popular science, with no clear cut-off point to indicate where popularization begins. Here, popularization actions are defined by scientists themselves, according to the following operational criterion: popularization means a wide audience, actions aiming at the non-specialized public. For more details, we refer the reader to Appendix and Jensen and Croissant (7). Industrial collaborations include funding from non-academic sources (8% of the activities), patents (6.%) and licenses (.%). The first subcategory (funding from non-academic sources) includes funding from contracts with industrial partners (the dominant category for the natural sciences) or funding from other nonacademic partners (such as regional funds). Teaching is only characterized by the annual number of hours dedicated to it. CNRS researchers have no teaching duties. We have described previously (Jensen et al., forthcoming) how we managed to obtain a large but robust database of bibliometric indicators of CNRS scientists. Briefly stated, our method used the author search of the Web of Science (WoS) on the subset of 87 scientists who had filled out the CNRS individuals report in all of the years 6. We excluded researchers in social sciences (their bibliographic records are not well documented in WoS) and in high energy physics (there were too few records in the CNRS database), leading to 69 names. After filtering out problem records where, for example, it was not clear whether the information was all for one person or for two with the same name, we obtained a database of 69 scientists with 8 Science and Public Policy August 8
3 reliable bibliometric indicators, as checked by close inspection of several hundred records. A more detailed description of our method is given in Appendix and Jensen et al. (forthcoming). We have used several bibliometric indicators as proxies for traditional academic research performance: number of papers published since they started their professional career, the average number of papers published per year since they started their professional careers, number of citations or Hirsch index (h) (Hirsch, ). It could be argued that h by itself is not a good measure when comparing the scientific output of researchers with different career lengths. A more relevant measure might be age divided by the career length in years (h y ) (Hirsch, ), although we have shown that it is not perfect either, since its average value for CNRS scientists decreases with the scientist s age and this complicates the later statistical analysis (Jensen et al., forthcoming). However, since h y is closer to a constant than h for scientists with different career lengths (Jensen et al., forthcoming), we will use it, along with the average number of papers published per year (which, in our data, does not depend on the scientist s age) and other bibliometric indicators. Proportion of scientists engaged in wider dissemination A summary of the subdisciplines encompassed by our database, together with some characteristic average values, is shown in Tables and. Figures and show the proportion of active scientists by age and grade, for each of the three wider dissemination activities. Overall, the CNRS scientists carried out more than 7 popularization activities and more than industrial collaborations per year, and these figures are increasing. Table shows, however, that the activity is very unequally distributed: over a three-year period, about half of our scientists remained inactive in popularization or industrial collaborations. More details on popularization activities, have been given in our previous analyses (Jensen, ; Jensen and Croissant, 7). These large scientific domains are in fact heterogeneous. It is interesting to study more disaggregated data, at the discipline level (corresponding to the CNRS scientific sections ). Table shows in detail the proportion of scientists in each of the subdisciplines engaged in popularization, teaching and industrial collaborations. For example, life science scientists are, overall, among the least active in popularization (6% in Table ). The subdiscipline level sheds some light on this surprising result. Scientists from the behavior, cognition and brain and biodiversity sections are much more active (around 6 6%) while the rest, working on more fundamental, esoteric fields, are even less active than chemists. Academic achievement of scientists engaged in wider dissemination Many scientists view wider dissemination activities (that is, going beyond papers in journals, conference presentations etc.) as a low status occupation, done by those who are not good enough for an academic career (Royal Society, 6). This common perception was captured by the well-known Sagan effect : popularity and celebrity with the general public are thought to be inversely proportional to the quantity and quality of the real science being done (Hartz and Chappell, 997). Biographers of the American astronomer Carl Sagan (Shermer, ) have shown that Harvard s refusal of Sagan s bid for tenure, and the National Academy of Science s rejection of the nomination of Sagan for membership, was a direct result of the belief in this effect. By analyzing his publication record, they have also shown that there is no such effect: Throughout his career, which began in 97 and ended in December 996, upon his untimely death, Sagan averaged a scientific peer-reviewed paper per month. The Sagan effect, at least when applied to Sagan himself, is a Table. Percentage of inactive (no action or no teaching, respectively), active (less than outreach actions or less than industrial collaborations or less than teaching hours, respectively) and very active scientists for the various CNRS scientific sections. This division into subpopulations is more instructive than the mean number of actions, as the activity is very unequally distributed among researchers: the % most active account for half of the actions (Jensen, ). Figures correspond to the total activity in the period 6. CNRS researchers do not have any teaching duties Domain Popularization Industrial collaboration Teaching Inactive Active Very active Inactive Active Very active Inactive Active Very active Physical sciences High energy physics Life sciences 6 66 Engineering Chemistry Earth sciences, astrophysics Social sciences All CNRS Science and Public Policy August 8 9
4 Table. Details of Table by subdiscipline. The precise names of the CNRS sections have been shortened for simplicity. The discipline high energy physics of Table corresponds to interactions, particles and strings As in Table, we show the percentage of active (less than popularization actions or less than industrial collaborations or less than teaching hours, respectively) and very active scientists (i.e. more active than the previous figures) for the CNRS scientific subdisciplines. For simplicity, we have not shown the percentages of inactive (no action or no teaching, respectively) scientists. These can be easily calculated from the difference to % of the sum of the two columns active and very active. Figures correspond to the total activity in the period 6 Subdiscipline Popularization Industrial Teaching Active Very active Active Very active Active Very active Physical sciences 6 Mathematics Physics, theory and method Interactions, particles and strings Atoms and molecules Condensed matter: dynamics Condensed matter: structure Engineering Chemistry 6 Information science Micro and nano-technologies Materials and structure Fluids and reactants Super/macromolecular systems Molecular architecture synthesis Physical chemistry Coordination chemistry Materials chemistry Biochemistry Earth sciences, astrophysics Solar systems and the universe Earth and earth plants Earth systems: superficial layers Continental surface Life sciences Molecular basis of life systems Genomic organization Cellular biology Cellular interaction Physiology Development, evolution Behaviour, cognition and brain Integrative vegetal biology Biodiversity, evolution Therapy, pharmacology Human and social sciences Human evolution Ancient and medieval history Modern and contemporary history Languages, language and speech Philosophy Sociology Economics and management Society and cultures Environment and society Politics, power chimera (Shermer, ). In the following, we will test on a larger scale whether such an effect exists for CNRS scientists, i.e. whether scientists engaged in popularization perform less well academically than the average. To anticipate our conclusion: we find exactly the opposite correlation: scientists engaged with society are more active than average. Comparing bibliometric indicators of narrow and wider disseminating scientists We begin by comparing the average academic performance of scientists who disseminate widely with those who restrict themselves to the conventional journals and conferences. The precise question we investigate is: if we choose a scientist at random and ask her or him whether she or he is active in wider dissemination, does the answer tell us something about her or his academic performance? According to the common view quoted above, the answer should be that a wider disseminating scientist has, on average, a weaker academic performance, which should be reflected in lower bibliometric indicators. Our data shows exactly the opposite effect. Figure shows that activity in wider dissemination is correlated with higher academic indicators. Scientists inactive in both popularization and industrial collaborations (roughly %) have a lower academic performance (h y =.6), which also decreases for those who are also inactive in teaching (%, h y =.6). If one uses the average number of papers published per year, the conclusion is similar: Science and Public Policy August 8
5 Figure. Evolution of the proportion of scientists active in dissemination as a function of their age. Data correspond to the whole database, i.e. before filtering with bibliometric indicators the average value is.8, while dissemination-active scientists have significantly higher average values (popularization:.8, p-value.6, industrial collaboration., p-value <. 6, teaching:., p-value 8 6 ). A potential danger of this kind of general comparison is the well-known variability of average h y indexes among different scientific disciplines (Iglesias and Pecharromon, 7). Therefore, we calculated the differences in h y between scientists of the same discipline but engaged differently in dissemination activities. Our results (Table ) confirmed that widely disseminating scientists are always, on average, academically more active than the inactive ones, even if the smaller number of scientists investigated prevents the results from being statistically significant for some disciplines. It should also be noted that h y is not always the best indicator of academic activity. For engineering, the average number of papers published accounts better for promotion (Jensen et al., forthcoming). Taking this indicator, differences between Figure : Evolution of the proportion of scientists active in dissemination as a function of their position. Data correspond to the whole database, i.e. before filtering with bibliometric indicators Figure : Average h y for inactive, active, or very active scientists (see Table for the definitions) in the different dissemination activities. Researchers in the social sciences are excluded because their bibliographic records are not well documented in WoS. Variance tests on the indicators ensured that they were strongly significant (for popularization: F = 6.9, p-value =.; for industrial collaborations: F = 8.6, p-value =.. For teaching, active scientists have a significantly higher h y than the non-active, p-value =.). However, in contrast to the situation for dissemination, the very active ones have the same h y as the mean (the small difference is not statistically significant). Our data pointed to an optimal value of roughly teaching hours per year, additional hours lowered the value of h y. active and inactive scientists (in terms of wider dissemination) become significant in favour of active scientists (e.g. popularization-active scientists from engineering sciences have an average publication rate over their professional life of. papers per year, instead of.9 for the inactive ones, p-value =.7). Scientists active in all wider dissemination activities It is also interesting to look at the academic records of the scientists active in all three of the wider dissemination areas. They represent roughly % of our 69 database, which is much more than expected if the engagements in the three different dissemination activities (teaching, industrial collaboration and popularization) were uncorrelated (%). This points to an open attitude, which makes a scientist practicing popularization more prone to teach or establish industrial collaborations. This high percentage is contrary to what one could expect from a time consumption argument, where each of these activities lowers the activity in the others. From an academic point of view, scientists who participate in more than one dissemination activity are more active academically than those who carry out only one of them. The precise values are: scientists active in all three dissemination activities have a h y of.7 against.7 for the others (p-value =.), those active in industrial collaborations and teaching.7 against.69 (p-value =. ), those active in industrial collaborations and popularization.7 against.7 (p-value =.), those active in popularization and teaching.7 against.7 (p-value =.8). Science and Public Policy August 8
6 Table. Differences in scientific activity, as measured by the normalized Hirsch index, for different subpopulations, characterized by the strength of their dissemination activities. To simplify the presentation, we only keep two categories: inactive and active, the latter grouping the active and very active categories of Table. We also show the p-values obtained by a standard Welch two sample t-test and the number of scientists in each domain Inactive h y Active h y p-value Number of scientists Popularization Physical sciences * 669 Life sciences.7.8.8** 7 Engineering...8 Chemistry Earth sciences, astrophysics * 6 Industrial collaboration Physical sciences.6.78 < 6 *** 669 Life sciences.69.8 < 6 *** 7 Engineering.7..7 Chemistry ** 88 Earth sciences, astrophysics Teaching Physical sciences Life sciences.67.8 < 6 *** 7 Engineering.7.. Chemistry *** 88 Earth sciences, astrophysics Notes: Standard significance codes are used for the p-values: * for <., ** for <., *** for <. Dissemination activity of the best scientists One can also look at the dissemination performance of the (academically) most active scientists, taken as those whose h increases faster than their career time (h y > ) (totaling /6 of our CNRS researchers). They are more active in popularising (% of active instead of 7%, p-value =.), industrial dissemination (6% of active instead of %, p-value =.) and teaching (69% of active instead of 6%, p-value = 7.. The same correlations are found by discipline, even if, again, the differences are less significant, except for biology and physics. Dissemination activity of those who are not good enough Finally, one can investigate whether those who are not good enough for an academic career (Royal Society, 6) are the most active in wider dissemination. Our previous result suggested that this is not the case, this was confirmed by a statistical analysis. Taking as not good enough the % of our CNRS scientists with the lowest value for h y (lower than.), we find that these scientists are also less active in wider dissemination, the precise figures being 9.% active for popularization instead of.% for the rest of the scientists (pvalue =. i.e. not significant),.% active for industrial collaboration instead of 7.8% (p-value =.) and 6% active for teaching instead of 67% (p-value =.). Even stronger differences (all highly significant statistically) are found if the number of publications per career year is used as the bibliometric indicator, which could be more appropriate when comparing scientists of different ages (Jensen et al. forthcoming) (for example,.7% of the not good enough are active in popularization instead of.6% in the rest of the scientists, p-value =.). Which scientists are active in wider dissemination? In the previous section, we have shown that scientists engaged in wider dissemination, be it popularization, teaching or industrial collaborations, are academically more active than other researchers. To be precise, we have shown that if you randomly select a scientist and ask her or him whether she or he is active in dissemination, a positive answer is a sign of higher bibliometric indicators. We now investigate the separate effects of the available characteristics of our scientists on the probability that they are active in wider dissemination. This will help us to interpret the correlations between academic and wider dissemination activities (see next section). We conducted a statistical analysis in order to single out the individual effects of each one of these characteristics, all other things being equal. For example, for a (hypothetical) average researcher, we analyzed the effect of her or his position, i.e. how much it separately increases (or diminishes) her or his probability of being active in popularization, for example. Since the variable that we investigated is a logical variable (either active or inactive), we have used a standard logit regression model. In this model, the probability of being active is written as P(y i = ) = F(β x i ) where β is the vector of fitted coefficients and x i the vector of characteristics of scientist i (age, position etc.). The marginal effect of a variation of the variable x ik (where k refers to one of the characteristics) on the probability of being active can be written as P(y i = )/ x ik = F/ x ik (β x i ) = β k F (β x i ). In a logit model, F(z) = e z /( + e z ), which leads to F (z) = e z /( + e z ) = F(z)( F(z)). This function reaches its maximum for z =, which corresponds to an activity probability of., leading to Science and Public Policy August 8
7 a proportionnality coefficient of /. Therefore, a simple interpretation of the effect of a scientist s characteristic on her or his probability of being active is as follows: the maximum marginal effect of a characteristic equals the corresponding coefficient divided by a factor. For example, the isolated effect of an age increase of one year is a decrease of about.7/ =.6% of the probability of being a popularizer. Being CR decreases the probability of being active in industrial collaborations by 8% compared to a DR sharing the same characteristics (age, sex, subdiscipline etc.). Our results are summarized in Tables and. The main influences of scientists characteristics, all other things being equal, are:. Position: as scientists reach higher positions, they become significantly more active in all dissemination activities.. Academic record: there is no significant influence except for industrial collaborations. For this activity, scientists with a higher Hirsch index are more active.. Age: wider dissemination activities decrease with age.. Gender: women are more active in popularization, men in teaching, and there is no significant difference in industrial collaborations. Results () and () represent a clear example of the usefulness of a regression study. Since age and position are strongly correlated, a simple study of the evolution of the proportion of active scientists with age is not conclusive. Figure confirms that Table : Binomial regressions to explain dissemination activities for 69 CNRS scientists (filtered database). Active means at least one action in the three-year period encompassed by our study ( 6). For popularization, this represents 9 scientists (.9%), for industrial collaborations 8 scientists (6.%) and for teaching 7 scientists (6.9%). These percentages are different from those of Table because social sciences are excluded from the filtered database. The explanatory variables are: sex, age, position, subdiscipline and Hirsch index (h) as bibliometric quantifier. The reference levels are: Condensed matter: structure for the subdiscipline and DR for the position. The columns give the coefficients of the fit, together with their significance. The position DRCE is almost never significant because it contains less than scientists Subdiscipline Press Open days Active in pop. Active in industrial Active in teaching Physical sciences 6 Engineering Chemistry 6 Earth sciences, astrophysics Life sciences Intercept -.8* -.7**.7.9***.*** h age CR CR DR DR DRCE Active in popularization Active in industrial collab. Active in teaching Male.7** -.7*** -.97** X.7*.**. -.* X..** *** -.8** -.* Reference..6 X.7***.6*** -.9*.9* -.*** -.*** -.7***.7***.96*.*** X.*** *** -.7*** -.9***.* -..9***.6*** X.*** Mathematics Physics, theory and method Atoms and molecules Condensed matter: dynamics Condensed matter: structure Information science Micro and nano-technologies Materials and structure Fluids and reactants Super/macromolecular systems Molecular architecture Physical chemistry Coordination chemistry Materials chemistry Biochemistry Solar systems, universe Earth and earth plants Earth systems: superficial layers Continental surface Molecular basis of life Genomic organization Cellular biology Cellular interaction Physiology Development, evolution Behavior, cognition and brain Integrative vegetal biology Biodiversity, evolution Therapy, pharmacology...**..*.*.*.* ***.6**.***.8** ***..***. -.6** * * -.** -.97* -.** -.9** ** * Reference ** -.9* -. -.* -.8**.***.6*.76*.7* -.*** -.*** -.*** -.7** -.69** -.7**.6* * -.*** -.6* -.8..***.***.9***.***.9***.86**..***.***.97*** -.*** **.***....6*.7.78**.7*.76*.***.96*** ***.69*.***.***.8**.7**.6.7.6*.8** -..9***.8.***.***.***.***.***.***.***.8***.79**.6***.8** Notes: Standard significance codes are used for the p-values: * for <., ** for <., *** for <. Science and Public Policy August 8
8 Table. Binomial regressions to explain dissemination activities for 69 CNRS scientists (filtered database). Active in all dissemination activities means at least one action in each of the activities over the three-year period and Inactive no action in any of the three dissemination activities. The explanatory variables are: sex, age, position, subdiscipline and Hirsch index (h) as bibliometric quantifier. The reference levels are: Condensed matter: structure for the subdiscipline and DR for the position. The columns give the coefficients of the fit, together with their significance Subdiscipline inactive for all Active for all Physical sciences Engineering Chemistry 6 Earth sciences, astrophysics Life sciences Intercept h age CR CR DR DR DRCE Male Mathematics Physics, theory and method Atoms and molecules, lasers and optics Condensed matter: organization and dynamics -6.** -..***.***.*** -.7** -. -.**.9.77* -..9 Reference 6 Condensed matter: structure Reference Information science and technology -.9** Micro and nano-technologies, electronics and photonics -.9*** Materials and structural engineering -.7** Fluids and reactants: transport and transfer -.*** Super/macromolecular systems, properties and functions Molecular architecture synthesis Physical chemistry: molecules and environment Coordination chemistry: interfaces and procedures Materials chemistry: nanomaterials and procedures Biochemistry Solar systems and the universe Earth and earth plants Earth systems: superficial layers Continental surface and interfaces Molecular basis and structure of life systems Genomic organization, expression and evolution Cellular biology: organization and function Cellular interaction Molecular and integrative physiology Development, evolution, reproduction and aging Behavior, cognition and brain Integrative vegetal biology Biodiversity, evolution and biological adaptation Therapy, pharmacology and bioengineering -.79* * *** -.9** -.* * *** -. -.** -.6.**.7** -.7*** -.7*** -.86***.6*** *.***.***.7***.7*** * *** ***.6.6***.8* Notes: Standard significance codes are used for the p-values: * for <., ** for <., *** for <. popularization activity decreases with age for all positions, but that scientists in higher hierarchical positions are much more active. It should be noted that there are strong positive correlations between different types of dissemination. As argued previously, this shows that, in practice, different activities do not compete for the scientist s time. Table also shows the specificities of the subdisciplines for the different types of wider dissemination activities. For example, mathematicians are much more active in teaching, and less in industrial collaborations. Table shows how scientists characteristics influence the probability of being active in all the dissemination activities. In general, the effects are similar to those seen for single activities, but the effects are stronger. The main difference is the strong effect of academic performance, which is even stronger than for industrial collaborations alone. This further confirms that the different activities (academic and wider dissemination) are not competing but tend to be mutually reinforcing. Table also shows the effects of age, gender etc. on the probability of being inactive in all dissemination activities. These effects are consistent (i.e. opposite) with those seen for the probability of being active, except Figure. Evolution of the proportion of scientists active in popularization as a function of their age, on average and for different positions. Data correspond to the filtered database, i.e. without social sciences and particle physics, which explains the differences with Figures and Science and Public Policy August 8
9 for the lack of a significant effect on academic performance. To interpret the correlations between academic and wider dissemination activities, one has to take into account the fact that our regression models quantify academic achievements in two different ways: the position and the bibliometric indicator, h. Since we include both in our regressions, the effect of h is considered all other things being equal, i.e. only within each position. Therefore, the lack of influence of h for the probability of being active in popularization (third column of Table ) does not contradict our previous findings (e.g. Table ). Indeed, since positions are strongly correlated with h and higher positions are much more active in popularization (Table ), our regression shows that that popularization activity is more influenced by the hierarchical position than by the bibliometric indicators within each category. The opposite is observed for the probability of participating in open day events (second column of Table ). Here, the position is not relevant (none significantly affects the participation even if a definite trend exists) but the Hirsch index is. A similar (but in the opposite direction) conclusion can be drawn for popularization in the press (first column of Table ): position is only slightly relevant (except for the youngest scientists, who rarely participate), but h is very important, as is the scientist s age. Briefly, the main influences for the other types of actions are the following: collaboration with industrial partners are strongly (positively) influenced by the position, Hirsch index and (decreasing) age. Instead, popularization through television and conferences or industrial collaborations through patents or licenses only depend on position and age and not bibliometric indicators. Finally, participation in popularization in schools decreases with age but is unaffected by the position or academic performance. Interpreting our results What can we learn from this statistical study of dissemination activities? We will examine different hypothesis and compare them with our findings. Our interpretations are centered on popularization practices. We will deal with industrial collaborations and teaching in future work. Hypothesis : Dissemination is done by those who are not good enough for an academic career. If we admit that bibliometric indicators are a good proxy for being good enough for an academic career, then our study clearly invalidates this hypothesis. First, randomly chosen, widely disseminating scientists have higher academic indicators than others. Second, all other things being equal, the probability of widely disseminating increases with academic position (Table ). Furthermore, better academic records increase the probability of being active in industrial collaborations. Hypothesis : Dissemination is done by people close to retirement. Our study has also shown that this hypothesis is incorrect. Figure shows that scientists close to retirement are less active in wide dissemination than average. Our statistical analysis also shows that, as their age increases, scientists become less active in wider dissemination, all other things being equal. Hypothesis : Popularization is driven by demand placed upon the scientific elite. This hypothesis assumes that popularization is driven by an external demand (institutions or journalists, for example). Then, the scientific elite, with higher bibliometric indices, is more visible from outside the scientific community, and is therefore more solicited. Our data supports this interpretation: scientists engaged in the type of popularization actions mostly driven by demand (radio, television, press, and to a lesser extent, conferences) have a higher h y than average (Table 6). Scientists involved in popularization activities that are mostly driven by the individual scientist, and are symbolically less important (open days, school conferences, web sites etc.) have a slightly lower average h y than the just-mentioned scientists (Tables and 6). Hypothesis : Active personality. We have repeatedly pointed out that being active in one activity is positively correlated with being active Table 6. Differences in scientific activity, as measured by the normalized Hirsch index, for different subpopulations, characterized by the type of their popularization activities. The first group (press, radio, public conference) refers to prestigious activities regulated mainly by the outside demand, whereas the second group (school conference, open days, web) gathers less prestigious activities mainly driven by scientists offer. The p-values give the statistical significativity of the differences. They are obtained by a standard Welch two sample t-test. Note that the differences for the first group are highly significant even if the number of scientists active in those activities is quite low: between (radio/television) and (public conferences) Type of action h y p-value Active Inactive Press *** Radio,.8.7. *** television Public * conference School conference Open days Web sites.7.7. Notes: Standard significance codes are used for the p-values: * for <., ** for <., *** for <. Science and Public Policy August 8
10 in the others. This suggests that the correlations observed could arise from some internal characteristic of the scientists involved, call it active personality or some aspect of intellectual capacity. For example, it could be argued that popularization is intellectually demanding, as it is difficult to explain complex scientific issues in simple terms. Therefore, good popularization demands a deep understanding of the subject, as anyone preparing lectures has experienced. This intellectual capacity would, in turn, generate higher academic records. Alternatively, one could interpret a widely disseminating personality as one keen to and able to sell her or his work, both to journalists and to those colleagues in charge of refereeing papers and citing them. This argument is supported by the fact that scientists active in all wider dissemination activities are also very active academically. Hypothesis : Social and cognitive hierarchies. The observed correlations between position and dissemination activities can also be understood by referring to sociological studies of scientific communities. Shinn (988) studied a French physics lab for several years, looking for correlations between hierarchical positions and cognitive work. He noticed a clear work division between junior and senior scientists. Junior staff devote most of their time to experiments or local questions. By local, Shinn means questions focused on particular points: a single experiment, or a thorough investigation of a very precise subtopic. In contrast, senior scientists devote most of their time to general questions, i.e. how the local results can be inserted into more global theoretical or conceptual frameworks. They also spend much time establishing and maintaining social networks both inside and outside the scientific community. Both these activities are clearly more in line with wider dissemination activities, which demand putting scientific problems into perspective. Moreover, senior scientists generally have a team of junior researchers working with them, who are active even when the seniors are away from the lab, disseminating. One could also argue that the scientific elite is able to transform its symbolic capital (Bourdieu, 98), gained in the academic arena, into public arenas, thus popularizing not only on issues directly related to their own domain but on virtually any issue. This would lead to a correlation between higher popularization activity and academic records, as for Hypothesis. In an old study of popularization practices of CNRS scientists, Boltanski and Maldidier (97) observed that senior scientists (Directeur de Recherche) have the legitimacy to speak to the public in the name of the institution. Scientists in the lowest positions can only express their own point of view, and popularization is mostly seen as a waste of time or a personal hobby. Hypothesis 6: Benefits of dissemination. This is the reverse causality from Hypotheses and. Dissemination activities compel scientists to open up their horizon, to discuss with people having other points of view on their research topics, giving new insights, contacts, which could improve their academic research. Actually, Hypotheses, and 6 could act together in a reinforcing way. It seems difficult to argue that this effect dominates, but it could contribute to the observed correlations, mainly in the case of industrial collaborations which strongly correlate with higher academic indicators. In summary, it is likely that the strong correlations observed between wider dissemination and academic performance result from the cumulative effects of Hypotheses 6. Are dissemination activities good for the career? It is commonly recognized that scientists engaged in dissemination do not receive much reward, and that their involvement may even be bad for their career (Royal Society, 6). In France, the CNRS director stated recently the importance of taking into consideration scientific culture popularization actions for the evaluation of researchers: one must insist that they give equal importance to scientific work and to activities related to the popularization and dissemination of scientific culture: participations in open door events, or the publication in magazines or other popularization works, in events organized for non-specialized audiences, newspaper articles or TV appearances etc. (letter sent to CNRS scientists in, our translation). In the document that was supposed to steer its long-term policy, the Multiyear action plan (CNRS, ), the CNRS thus declared that: If current [evaluation] practice is suitable for the purpose of evaluating academic research, the same cannot be said for interdisciplinary activities and for other facets of scientific work: transfer of scientific knowledge, teaching and popularization. Consequently, the work by CNRS researchers who choose to engage in these activities, which are very necessary for the CNRS, is not adequately acknowledged and researchers are therefore reluctant to proceed in this direction. Thanks to our large database, we were able to study statistically the influence of dissemination activities on the promotions of CNRS researchers to senior positions (Directeur de Recherche) over the period 6. Table 7 shows the results of our regression analysis, for all CNRS disciplines and for each discipline separately. It turns out that dissemination activities are not bad for scientists careers. They are not very good either: the effects are generally weak, but are positive, and rarely significative. The detailed study by discipline shows that the overall positive effect of popularization arises mainly from 6 Science and Public Policy August 8
11 Table 7. Binomial regressions to explain promotions to senior positions (from CR to DR) on the 86 candidates and 79 promotions from all scientific disciplines of our filtered database. The last column shows the regressions for the DR to DR promotion, on 76 candidates and 67 promotions from all scientific disciplines of our filtered database. The explanatory variables are: sex, age, subdiscipline (not shown to simplify since none is significative) and h as bibliometric quantifier (except for engineering, where the number of articles accounts much better for the promotions, see Jensen et al. (forthcoming)). The columns give the coefficients of the fit for each scientific domain, together with their significance All Physics ENG Earth Chemistry Life DR (Intercept) * ** Act pop Act indus Act teach...79** *.9* Male h (or art for ENG) Age Age -..***.*** -.*** ***.8* -.6*.7.8*.* -.6* * Notes: Standard significance codes are used for the p-values: * for <., ** for <., *** for <...** ***.78** -.7** its recognition in the life sciences and the positive effect of teaching from chemistry. However, it is interesting to note that all dissemination activities positively influence promotions for most of other disciplines, even if their effects are not significative. Overall, two characteristics have strong effects: academic activity (h or the number of papers) and age (the optimal age for becoming DR is 6.6 years, for DR.). For DR promotion, there is a small (and positive) effect from industrial collaborations. Discussion and conclusions Our statistical study of the correlations between dissemination activities and academic records of more than CNRS scientists from most disciplines excluding the humanities and social sciences, has allowed us to establish several facts. First, we have clearly shown that scientists engaged in wider dissemination also perform better academically, refuting the idea that wider dissemination activities are carried out by those who are not good enough for an academic career (Royal Society, 6). We have even shown that some prestigious activities (press, radio and television) are mostly carried out by the scientific elite in academic terms. One can certainly criticize the idea that bibliometric indicators account properly for the academic quality of scientists (Jensen et al. forthcoming; Kostoff, 998; Leydesdorff, 998; Liu, 99; Brooks, 986). However, those who argue that wider dissemination activities are carried out by the worst scientists usually accept this definition of scientific quality. Therefore, our paper should convince them that they are wrong. That was the easy part of the discussion. The interpretation of our results is otherwise not easy, as there have been few qualitative studies on the perception by scientists of popularization or voluntary teaching practices. Concerning relations with industries, a group at the Catholic University of Leuven (K.U. Leuven, Belgium) examined the academic records of scientist inventors at their university (Van Looy, 6). Their data suggest a reinforcing or positive spillover effect on scientific performance from engaging in technology development efforts, which is consistent with our findings in a much larger sample. A recent study has investigated the factors that predict scientists intentions to participate in public engagement (Poliakoff and Webb, 7). Thanks to a questionnaire distributed to academic staff and postgraduates, it was found that the main reasons why scientists decided not to participate in public engagement activities are the following: they had not participated in the past (a result consistent with our former finding (Jensen and Croissant, 7)), they have a negative attitude toward participation (it is seen as pointless or unenjoyable ), they feel they lack the skills and finally that they do not believe that their colleagues participate in such activities, which is interpreted as an indicator of the relative irrelevance of this activity. Notably, the lack of time or career recognition are not seen as important determinants of participation. Clearly, more qualitative studies on the relations of the scientific milieu and dissemination practices are needed. Here, we limit our discussion to three main issues: First, what have we learnt about the relationship of scientists to popularization? Our study suggests that popularization is mostly an activity of the (academically speaking) scientific elite. Our finding agrees with data from the Royal Society survey (Royal Society, 6), which showed that higher positions popularize more, the differences being even larger than in our database: 86% of senior staff were involved, while a mere % of the junior staff joined in. The following Royal Society report comments on the difference in involvement of senior and junior staff: The seniority finding is borne out by the qualitative research which found that young researchers keen to climb the research career ladder were focused on research and publishing and/or felt that they needed more experience before they could engage with those outside their research community. We may add (Hypothesis above) that senior Science and Public Policy August 8 7
12 activities are more in line with dissemination that those carried out by junior staff. The question that our findings raise is then: why does a significant fraction of the scientific community feel that only bad scientists popularize? Is it a problem of jealousy for colleagues that manage to present their results to a wide audience? Is that because creating knowledge is judged more important than widely disseminating it, as has been suggested by Shinn (988)? This would imply that many scientists are still prisoners of the diffusion model (Weigold, ), which ignores that to disseminate knowledge, one has to recreate it, an altogether creative and difficult endeavour. Qualitative interviews indicate that many reasons push scientists to engage in popularization. In private discussions, popularizers acknowledge that one of the main reasons is the pleasure of interacting with the public, of going out of the lab (Pérez et al., 8). For the Royal Society study (Royal Society, 6) i.e. in a more official environnment, the strongest justification for popularization is informing the public. We can wonder whether scientists are still prisoners of the so-called deficit model (Weigold, ). This is an old model for scholars of the science studies field, dating back to 96. It insists on the teaching of elementary scientific facts and methods to the public. Listening to the public seems important to only a few percent of the scientists interviewed in the UK (Royal Society, 6). However, this idea should be one of the strongest with a more generous vision of the public in mind (Lévy-Leblond, 99; Wagner, 7). Scientists also seem to ignore the numerous criticisms of the deficit model: the relation between the knowledge of scientific facts and its appreciation is empirically unsolved, and the knowledge of the facts of science taken out of their context is more alienating than it is informative. It is also important to establish links in the other direction, where scientists learn from society (Irwin and Wynne, 996; Jurdant, 99; Bensaude-Vincent, ). Much of the culture of the scientific milieu seems far from these ideas at the moment. Second, what do we learn about the relationship between science and society? Our study has shown that, even in the institution hosting the most fundamental sciences, roughly half of the scientists are in close contact with society, i.e. they popularize or look for funding outside the academic sphere. This could worry some fundamentalists who think that science can only work by developing its own questions, protected from the emergency and the deformation congenital to the social and economics worlds. However, our result will not surprise scholars in science studies, as they know that the ivory tower of science never existed. Scientists have always been engaged with society, on which they depend for funding (e.g. Biagioli, 99; Pestre, ; Latour, 988). Even the most fundamental physical theories such as relativity (Galison, ) and quantum mechanics (Hoddeson et al., 99) have been inspired by applications. Finally, another contradiction between our study and common views among scientists is the idea that wide dissemination activities are negative for the career. For example, % of scientists of the Royal Society survey (Royal Society, 6) answered that scientists who engage in popularization are viewed less well by their peers. Here, we have shown that promotion is mainly determined by academic performance, wide dissemination activities being marginal, counting only in specific disciplines (chemistry for voluntary teaching, life sciences for popularization). However, there is no negative effect. How can we explain the common opposite idea that pervades some of the scientific community? We started our paper by proclaming that many prestigious institutions claim that dissemination activities are priorities. We have shown that these activities are carried out by academically active scientists, that receive no reward for their engagement. We feel that the institutions now have a duty to invent ways of evaluating and rewarding the scientists who are active in widely disseminating their work. Appendix. A summary of popularization activities of CNRS scientists To draw up their personal annual report, CNRS researchers must specify the type of any popularization activities that they have performed. Table 8 shows a distribution of types of activities according to the different scientific departments of the CNRS for the 6 data. The categories are chosen by the scientists themselves. Most category names in Table 8 are self-explanatory. Associations refer to popularization actions taken to help associations understand the scientific aspects of their activity (think of patients or astronomical associations). Schools refers to actions taking place in schools. Web to popularization sites on the web. It is interesting to analyze the representation of certain disciplines for each type of activity. For instance the relatively high representation of social sciences researchers in radio/television and, to a lesser extent, in activities involving associations, the press and conferences. Not surprisingly, these researchers are by far under-represented in open door events. On the other hand, their weak presence in schools is food for thought for the community. Nuclear physics, chemistry and engineering departments are relatively highly represented in open door activities, which are relatively scarce in the life sciences. These departments are rather absent from engagement with the press, radio or non-professional publishing. (continued) 8 Science and Public Policy August 8
13 Appendix (continued) Table 8 Distribution of popularization activities undertaken by scientists in various departments at CNRS Chemistry Nuclear Earth Life Social Engineering Physics Info tech Other Conference Exhibition Associations Schools Books/CD Rom Open door Press Radio/television Web Appendix. Obtaining reliable bibliometric indicators for the CNRS scientists We detail our procedure to obtain a large but reliable sample (around records) of bibliometric indicators (number of publications, citations and h index). The difficulty lies in the proper identification of the publications of each scientist. Two opposite dangers arise. The first one consists in including extra publications because the request is not precise enough. For example, if only surname and name initials are indicated to WoS, the obtained list may contain papers from homonyms. The second one consists in missing some papers. This can happen if scientists change initials from time to time, or if the surname is that of a woman who changed her name on marriage. But this can also happen when one tries to be more precise to correct for the first danger, by adding other characteristics such as scientific discipline or French institutions for CNRS scientists. The problem is that both the records and the WoS classifications are far from ideal: the scientific field can be confusing for interdisciplinary research, the limitation to French institutions incorrect for people starting their career in foreign labs etc. Basically, our strategy consists in guessing if there are homonyms (see below how we manage to obtain a good idea about this). If we think there are no homonyms, then we count all papers, for any supplementary information (and the resulting selection) can lead to some records being missed. If we guess that there are homonyms, then we carefully select papers by scientific domain and belonging to French institutions. After all the bibliometric records have been obtained in this way, we filter our results to eliminate suspect records by two criteria: average number of publications per year over the person s research career, and age at the first publication. Evaluate the possibility that there are homonyms For this, compute the ratio of the number of papers found for the exact spelling (for example JENSEN P.) and all the variants proposed by WoS (JENSEN P.*, meaning P.A., P.B. etc.). If this ratio is large (in our study, larger than.8), then the studied surname is probably not very common and the author might be the single scientist publishing. To obtain a more robust guess, we used the scientist s age. We looked at the total number of papers and compared it to a maximum normal rate of publishing, taken to be six papers a year. If the publishing rate was smaller than our threshold, this was a further indication that there was a single scientist behind all the records. Actually, our strategy can be misleading only when there are only homonyms with the same initial and all the homonyms have published very few papers. Obtain the bibliometric records No homonyms If we guess that there is a single scientist behind the publications obtained for the surname and initials (which happens for about 7% of the names), we record the citation analysis corresponding to all associated papers. Homonyms If we estimate that there are homonyms, we try to eliminate them by using the supplementary data that we have. We refine the search by scientific field ( subject category in WoS terms, but one can select only one) and by only selecting French institutions. Eliminate suspect records Finally, once all the data has been gathered according to the preceding steps, we eliminate suspect results by two criteria related to the scientist s age. For a record to be accepted, the age of the scientist on first publication has to be between and years, and the average number of publications per year over their career, between. and 6. After this filtering process, we end up with 69 records out of the 69 initial scientists, i.e. an acceptance rate of %. Can we understand why half of the records are lost? First of all, let us detail how the different filters eliminate records. Deleting scientists who published their first paper after years old eliminates 7 suspect names, which are probably related to errors or missing papers in the WoS database, to married women for whom we miss the first papers published under their maiden name and to (continued) Science and Public Policy August 8 9
14 Appendix (continued) people who started their career in non-french institutions and had homonyms. Deleting scientists who published their first paper before years old eliminates additional suspect names, which are probably related to errors in the WoS database, to scientists with older homonyms which we could not discriminate. Deleting scientists whose record contained less than. papers per year on average leads to the elimination of names. These wrong records can be explained by the method missing some publications, as in the case first publication after years old. Deleting scientists whose record contained more than 6 papers per year on average leads to the elimination of a further 78 names. These doubtful records can be explained by the presence of homonyms we could not discriminate between. Finally, to make our database more robust, we decided to eliminate records suspected of containing homonyms even after selection of discipline and institution. This was done by eliminating the 9 scientists for which the number of papers kept after selection was smaller than % of the total number of papers for the same surname and initials. In those cases, we did not trust our selection criteria sufficiently to keep such a fragile record. A robust bibliometric database In summary, our method leads to a reliable database of around scientists from all hard scientific fields. It discriminates against married women who have changed their surname. It also suffers from the unavoidable wrong WoS records. We stress that the main drawback to eliminating half of the records is the resulting difficulty in obtaining good statistics. But at least we are pretty confident about the robustness of the filtered database. Our filtering criteria were based on homonym detection, age of first publication and publication rate. The first criteria correlates only with scientist s surnames, therefore we can expect that it introduces no bias except for married women. Actually, there is a lower proportion of women after filtering:.9% women in the 69 selection, against 9.6% in the 69 database. This is consistent with the preferential elimination of married women who changed surnames and have an incomplete bibliographical record. The two other criteria could discriminate scientific disciplines with lower publication rates or that are underrepresented in WoS. For example, more scientists from the engineering department have been eliminated in the filtering. The mean age is somewhat lower in the filtered database (6. years) to be compared to 6.8 in the whole dataset, probably because the records from older scientists have a higher probability of containing errors. However, overall, the filtered database is very similar to the initial one. For example, the percentage of candidates for senior positions is 6.% in the 69 selection, against 6.% in the 69 database, and the percentages actually promoted are, respectively,.9% and.%. The proportions of scientists from each position is also similar: none of the small differences between the filtered and unfiltered values are statistically significant. As noted previously, the robustness of our filtered database is validated by the significantly better indicators found for scientists in higher positions. An even stronger test (because the effect is subtler) resides in testing the correlations of the scientist s age at his or her first publication with several variables: age, position, subdiscipline and gender. We find a progressive decrease in the age of first publication when a scientist has a higher position (all things being equal, for example scientist s age), an effect that is intuitively appealing but certainly small. The fact that we can recover such a subtle effect is a good indication of the robustness of our procedure. We also recovered the intuitive effect of the scientist s age (older scientists have begun their career later). The gender effect (men publish their first paper two months later than women, all other things being equal) is more difficult to interpret, since it mixes many effects: our discrimination (in the filtering procedure) of married women, the unknown effects of marriage and children on scientists careers etc. Finally, a word of caution: our database is robust in a statistical sense, and allows us to characterize the academic records of different scientists groups (by discipline, position etc.). Bibliometric data alone should not be used to compare individuals. Notes. The statistical analysis was carried out with the open software R. Available at < last accessed on July 8.. If positions are omittted from the regression, h becomes strongly significant.. Unfortunately, WoS allows the selection to be made only on the institutions of all coauthors as a whole. So we might retain articles of homonyms that have coauthored a paper with a French scientist.. For a noticeable fraction of scientists, WoS records only start in the 99s, even if there are much older publications, which can be found by Google Scholar and other similar routes. References Allix, J P 7. Sciences et société en mutations. Editions du CNRS, 7. Bensaude-Vincent B. A genealogy of the increasing gap between science and the public. Public Understanding of Science,, 99. Biagioli, M 99. Galileo, Courtier: The Practice of Science in the Culture of Absolutism. Chicago, IL: University of Chicago Press. Boltanski, L and P Maldidier 97. Carrière scientifique, morale scientifique, vulgarisation. Information sur les science sóciales, IX(), 99 8 Bourdieu P 98. Distinction: A Social Critique of the Judgement of Taste. London: Routledge. Brooks, T A 986. Evidence of complex citer motivations. Journal of the American Society for Information Science, 7, 6. Cheveigné, S 7. Private communication, 7. CNRS. Dissemination is one of the official duties of CNRS researchers. furthermore, in its long-term policy engagement with the French state, CNRS recognizes the importance of science dissemination and its proper evaluation. Available at < last accessed on July 8. Galison, P. Einstein's Clocks, Poincaré's Maps. London: Sceptre. Hartz, J and R Chappell 997. Worlds Apart: How the Distance Between Science and Journalism Threatens America's Future. Nashville, TN: Freedom Forum First Amendment Center. Hilgartner, S 99. The dominant view of popularization: Conceptual problems, political uses. Social Studies of Science,, 9 9. Hirsch, J E. An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences,, Available at < org/cgi/content/abstract//6/669>, last accessed on July 8. Hoddeson, Lillian, Ernst Braun and Spencer Weart eds. 99. Out of the Crystal Maze: Chapters from the History of Solid State Physics. Oxford: Oxford University Press Iglesias J E and C Pecharromán 7. Scaling the h-index for different scientific ISI fields. Scientometrics, 7,. 7. Irwin, A and B Wynne 996. Misunderstanding Science? The Public Reconstruction of Science and Technology. Cambridge: Cambridge University Press. Jensen, P. Who's helping to bring science to the people? Nature,, 96. Jensen, P and Y Croissant 7. CNRS researchers' popularization Science and Public Policy August 8
15 activities: a progress report. Journal of Science Communication, October 7. Available at < last accessed on July 8. Jensen, P, J B Rouquier and Y Croissant forthcoming. An empirical test of bibliometric indicators. Scientometrics. Jurdant, B 99. Popularization of science as the autobiography of science. Public Understanding of Science,, 6 7. Kostoff, R N 998. The use and misuse of citation analysis in research evaluation. Scientometrics, (), 7. Available at and < akademiai.com/content/ev7tgth7r7>, last accessed on July 8. Latour, B 988. Science in Action: How to Follow Scientists and Engineers through Society. Cambridge, MA: Harvard University Press. Lévy-Leblond, J-M 99. About misunderstandings about misunderstandings. Public Understanding of Science,, 7. Leydesdorff, L 998. Theories of citation? Scientometrics,,. Liu, M 99. The complexities of citation practice: a review of citation studies. Journal of Documentation, 9, 7 8. Pérez, A, P Kreimer and P Jensen 8. Scientists motivations for popularizing science in Argentina. In preparation. Pestre, D. Science, argent et politique. Paris: INRA Editions. Poliakoff, Ellen and Thomas L Webb 7. What factors predict scientists' intentions to participate in public engagement of science activities? Science Communication, 9, 6. Shermer, M B. This view of science: Stephen Jay Gould as historian of science and scientific historian, popular scientist and scientific popularizer. Social Studies of Science, (), 89. Shinn, T 988. Hiérarchies des chercheurs et formes des recherches. Actes de la recherche en sciences sociales, 7,. Royal Society 6. Factors affecting science communication: a survey of scientists and engineers, 6. Available at < last accessed on July 8. Van Looy B, J Callaert and K Debackere 6. Publication and patent behaviour of academic researchers: Conflicting, reinforcing or merely co-existing? Research Policy,, Wagner, W 7. Vernacular knowledge. Public Understanding of Science, 6, 7. Web of Science (WoS). Available at < last accessed on July 8. Weigold, M F. Communicating science. Science Communication,, 6 9. Science and Public Policy August 8
16 Science and Public Policy ISSN -7 Science and Public Policy is a refereed, international journal on policies for science, technology and innovation, and on the implications of science, technology and innovation for other areas of policy. It covers all science, technology and innovation for other areas of policy. It covers all science and technology (basic, applied, high, low, or otherwise) and all countries. It is read in around 8 countries, in universities, government ministries and agencies, consultancies, industry and elsewhere. Editors Dr David H Guston, Consortium for Science, Policy and Outcomes, Arizona State University, PO Box 87, Tempe, AZ 887-, USA; Dr Susana Borrás, Center for Business and Politics, Copenhagen Business School, Steen Blichersvej, Frederiksberg, Denmark; Consulting editor: Professor Susan Cozzens, School of Public Policy, Georgia Institute of Technology, Atlanta, GA -, USA Associate editor: Daniel Barben, Consortium for Science, Policy and Outcomes, Arizona State University, PO Box 87, Tempe, AZ 887-, USA; Book reviews editors Prof Cooper Langford, Science, Technology & Society Program, University of Calgary, Calgary, Alberta, Canada TN N; Dr Jakob Edler, PREST and Institute of Innovation Research, Manchester Business School, Manchester, M 9PL, UK; Publisher William Page, Beech Tree Publishing, Watford Close, Guildford, Surrey. GU EP, UK Tel: Fax: Website: with links to journal articles on Ingenta Production assistants Janet Hodgkinson Trisha Dale Editorial advisory board Mario Albornoz, Centre for Studies of Science, Development and Higher Education, Buenos Aires, Argentina Daniele Archibugi, a Director of the National Research Council, Italy Anthony Arundel, UNU-MERIT, The Netherlands Phillip Cooke, Advanced Studies, University of Cardiff, UK Paul Cunningham, PREST, Manchester Business School, UK Charles Edquist, CIRCLE, Lund University, Sweden Shulin Gu Tsinghua, University, Beijing, China David Hart, Public Policy, George Mason University, USA Ron Johnston, Executive Director, Australian Centre for Innovation and International Competitiveness, Sydney, Australia Calestous Juma, Co-ordinator, UN Millennium Project Task Force on Science, Technology and Innovation, Kennedy School of Government, Harvard University, USA Gary Kass, Parliamentary Office of S&T, UK Stefan Kuhlmann, School of Management and Governance, University of Twente, The Netherlands Philippe Larédo, ENPC, Paris, France Kong-Rae Lee, STEPI, South Korea Rolf Lehming, Science Resources Statistics, NSF, USA Loet Leydesdorff, University of Amsterdam, Netherlands Angela Liberatore, European Commission, Belgium Elena Mirskaya, Russian Academy of Sciences, Moscow, Russia Judith Mosoni-Fried, MTA KSZI, Budapest, Hungary Johann Mouton, CREST, Stellenbosch University, South Africa Richard R Nelson, Columbia University, USA Helga Nowotny, Vice President, European Research Council Hiroyuki Odagiri, Economics, Hitotsubashi University, Japan Howard Rush, CENTRIM, Freeman Centre, Brighton, UK Luis Sanz-Menéndez, Deputy Director-General, Ministry of S&T, Spain Judith Sutz, University Research Council, Universidad de la República, Uruguay Kevin Urama, African Technology Policy Studies Network, Kenya Eric von Hippel, Head, Innovation and Entrepreneurship Group, MIT/Sloan School of Management, USA Lea Velho, University of Campinas, Brazil Bruno van Pottelsberghe, former Chief Economist, European Patents Office, now Free University of Brussels, Belgium Subscription information, see inside back cover Typeset mainly in Times by Hilary Soper, Beech Tree Publishing, and printed by EntaPrint, Cranleigh, Surrey, UK.
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