dataesr/scientific-paragraphs-categorization huggingface dataset:
Data We rely on the funding acknowledgment information reported in scientific articles to identify grant-funded and non-grant-funded publications  (Rigby, 2011; Gok et al. 2016; Grassano et al. , 2016). Specifically, we retrieve the acknowledgment information from the Web of Science (WOS) bibliometric dataset provided by Clarivate 4. We use Microsoft Academic Graph (MAG) citation data to assess the publication impact. We construct our universe of publications by collecting all the 481,536 scientific articles published between 2009 and 2013, having at least one author affiliated with a French institution and a Digital Object Identifier (DOI). We choose 2009 as the starting date of our analysis because WOS acknowledgment data are reliable only starting from that date  (Mongeon and Paul-Hus, 2016; Mejia, 2017).
We choose 2013 as the end year because MAG citation data are unreliable after 2018 and, for the articles published in 2013, we need a 6-year forward citation window to evaluate their impact. We focus on scientific articles and exclude other types of publications, such as reviews or book chapters. Moreover, we limit our analysis to publications written in English because WOS collects acknowledgments only for those publications. Finally, we exclude journals 2 The largest public research organization in France is the Centre national de la recherche scientifique (CNRS). 3 Website: https://anr. fr/fileadmin/documents/2021/ANR-RA2020-en. pdf, page 04. 4 Web of Science (WOS) provides two acknowledgment fields, one reporting the raw text as written in the paper and the other is an artificial field that already extracted the names of the funding organizations from the raw text through an algorithm.
The WOS algorithm does not seem to be accurate, thus we rely on the raw text of the acknowledgments. in Social Sciences and Humanities because, for these disciplines, WOS reports reliable acknowledgments only starting from 2015  (Álvarez-Bornstein et al. , 2017; Liu et al. , 2020). After applying all the previously mentioned constraints, we obtained a sample of 283,873 scientific articles. We retrieve articles' and authors' characteristics used for the matching identification strategy from Elsevier's SCOPUS database. We add information on the authors' gender by matching the authors' given names with French 5  and international gender-name databases 6. We use the QS university ranking 7  to characterize the prestige of the authors' affiliations. Our sample of 283,873 publications can not be promptly matched with the complementary information retrieved from these databases.
Identifying grant-funded articles and non-grant-funded articles From our sample of 195,435 articles, we select all 23,950 articles acknowledging at least one ANR grant among the funding sources (12. 25% of the publications in our sample). To avoid mixing the effects of several funding sources contributing to the research outcome described in an article, we limit the sample of grant-funded articles to the 6,441 publications reporting only ANR grants as the funding source. In other words, we excluded the articles acknowledging other non-ANR grants in addition to an ANR grant 8. To identify publications without grant funding, we considered articles that do not report any acknowledgment. The logic is that if no grant acknowledgment is reported in an article, the research outcome described in the article is likely to result from government block funding used to pay the researcher's salary and the lab equipment.
Methodology Our analysis compares the impact of articles resulting from the support of ANR grants with the impact of articles not supported by any grant. We measure the articles' impact in the short and long run. To measure the impact in the short run, we count the number of yearly citations received by an article in the first three years after its publication, i. e. , from year t to t+2, where t is the year of publication. To measure the impact in the long run, we count the number of yearly citations received by an article from year t+3 to t+5. A simple comparison between the number of citations received by the 6,441 grant-funded and the 76,615 non-grant-funded articles is likely to be affected by a selection bias  (Jaffe, 2002). Indeed, grant-funded and non-grant-funded articles might systematically differ in other aspects than the funding source.
For example, the articles' or authors' characteristics, such as collaborative behaviors, team composition, and stock of knowledge, might relate both to the likelihood of observing grant-funded research and to the articles' impact  (Wuchty et al. , 2007; Ebadi and Schiffauerova, 2015; Mukherjee et al. 2017; Bol et al. , 2018; Bianchini et al. , 2022). Therefore, we adopt a Propensity Score Matching (PSM) procedure that relies on the "nearest neighbor" approach to mitigate the potential selection bias. This approach compares each of the 6,441 articles funded by an ANR grant with a similar article without grant funding. The similarity between articles is assessed by a probabilistic score based on the articles' and authors' observable characteristics. Specifically, we run a logistic regression where the left-hand-side variable is represented by the dummy variable Grant-funded which equals one if the article acknowledges an ANR grant as the unique funding source, and zero if it does not report any acknowledgment.
Year of publication The publication year of the article. Table  2  shows the average marginal effects calculated from the coefficients estimated with a logistic regression. We find that articles with 2 to 4 authors have a 4. 4 percentage point higher probability of acknowledging an ANR grant than single-author articles. Articles with more than four authors have a 5. 8 percentage point higher probability of acknowledging an ANR grant than single-author articles. Interestingly, we find that the presence of an international author among the article's authors is associated with a 7. 3 percentage point lower probability of acknowledging an ANR grant than articles without international authors. Moreover, the presence of a female author is associated with a lower probability that an ANR grant is acknowledged by the article (-0.
71 percentage points), while the presence of an author affiliated with a top-ranked university is associated with a 2 percentage point higher probability to acknowledge ANR. Moreover, articles reporting multiple affiliations are associated with a 1. 1 percentage point higher probability of acknowledging ANR. Concerning the article's characteristics, articles belonging to the fourth quartile of the backward citations distribution are 6 percentage points more likely to acknowledge ANR grants than articles belonging to the first quartile. Finally, articles published in Mathematical and Physical journals are more likely to acknowledge an ANR grant than those published in Life sciences, Medicine, and Engineering. Using the estimates reported in Table  2 , we predict the probability of an article to acknowledge an ANR grant.
According to these predictions, for each of the 6,441 articles funded by ANR, we match an article drawn from the sample of 76,615 articles without grant acknowledgments and with the most similar probability of acknowledging ANR. In other words, we select the "nearest neighbor" to the grant-funded article according to the propensity score value. Figure  1  reports the histograms of the density of propensity scores for the articles before (raw grant-funded versus raw non-grant-funded) and after matching (matched grant-funded versus matched non-grant-funded). We observe that after the matching, the propensity score distribution of the grant-funded articles is almost identical to the propensity score distribution of the non-grant-funded articles. The similarity between the two distributions after the matching ensures the quality of the matching exercise.
In doing so, we obtain a sample of 3,416 articles in Life sciences and Medicine (1,708 grant-funded and 1,708 non-grant-funded articles, respectively), 3,612 articles (1,806*2) in Mathematics, 2,978 articles (1,489*2) in Engineering, and 7,438 articles (3,719*2) in Physical sciences, for a total of 17,444 articles. This latter number is higher than the one reported in Table  4  (12,882=6,441*2) due to the double counting of articles published in journals classified in more than one field. Table  5 , Column 1, shows that in the short run, articles in Mathematics acknowledging ANR grants receive, on average, 13. 08% fewer citations than articles not supported by grants. On the contrary, grant-funded articles in Engineering receive 8. 15% more citations than non-grant-funded articles. Looking at the other research fields in the short run, we do not find any statistically significant difference in the number of citations received by grant-funded and non-grant-funded articles.
11 See Appendix H for a detailed explanation of the methodology used to classify articles in research fields. Table  5 , Column 2, shows that in the long run, articles acknowledging ANR grants benefit from a higher impact than articles without grant support in three research fields: Life sciences and Medicine, Engineering, and Physical sciences. Life sciences and Medicine and Engineering, show the largest citation gap between grant-funded and non-grant-funded articles, +15. 06% and +13. 87%, respectively. In Physical sciences, the citation gap in favor of grant-funded articles equals +7. 67%. Interestingly, in Mathematics, grant-funded and non-grant-funded articles do not show any citation gap in the long run. In Appendix A, we report the tables showing the logit estimates of the articles' probability of acknowledging an ANR grant and the covariate balance tables pre-and post-matching for each of the four research fields analyzed.
Using SCOPUS citation data In Appendix C, we calculate the articles' impact using the SCOPUS bibliometric dataset instead of Microsoft Academic Graph as the citation data source. SCOPUS does not provide punctual information on the citations received by an article each year, but only the aggregate number of citations at the moment of the data query (in our case, 2019). Therefore, we calculate the SCOPUS average yearly citations retrieving the cumulated number of each article's citations in 2019, and dividing it by the years elapsed between the article's publication date and 2019. In doing so, we can not distinguish between the short and long run. Using SCOPUS as an alternative source of citation data, we find that grant-funded articles receive, on average, 4. 40% more yearly citations than articles without grant support (Table  C1 ).
33% more citations than articles not supported by grant funding in the long run. Given the similarity of the results with our main analysis and the loss of 904 observations, we refrain from reporting this analysis as our main analysis and consider it a robustness check. Appendix D reports the table of the average marginal effects of the articles' probability of acknowledging an ANR grant (Table  D1 ) and the covariate balance table pre-and post-matching (Table  D2 ), including the three new covariates.
Including articles supported by other competitive grants in addition to ANR grants In our main analysis, we compare the impact of articles supported by ANR grants with those not supported by grants. To select articles supported by ANR grants, we look at those that acknowledge only ANR as the unique source of grant funding. In doing so, we exclude all the publications resulting from ANR grants bundled with other competitive grants. In this robustness check, we consider as ANR grant-funded articles all the articles acknowledging at least one ANR grant, allowing for the presence of other non-ANR grant funding sources. We expect that considering multiple funding sources in our study sample will boost the positive impact of grant funding on the articles' number of citations received. Indeed, if the increase of citations observed for ANR grant-funded articles is due to the grant's additional financial resources, having grants other than ANR should further increase the articles' impact.
We include in our sample all the articles supported by grants distributed by the ANR agency between 2009 and 2013. Using a comprehensive set of articles' and authors' characteristics, we propose a Propensity Score Matching approach to assess the effect of grant funding. We find that articles supported by ANR grants receive +6. 93% more citations than articles not supported by grant funding in the long run, while the difference in the citations received is not statistically significant in the short run. This result can be interpreted in light of the work of  Wang et al. (2017) , showing that breakthrough novel articles outpace non-novel articles in the number of citations received starting from three years after publication. Like  Wang et al. (2017) , we find that ANR grant-funded articles are more impactful than non-grant-funded articles from three to five years after publication.
Interestingly, Mathematics is the only field in which articles resulting from grant funding are less impactful than non-grant-funded articles in the short run (-13. 08%). Moreover, in a robustness check, we show that articles supported by ANR grants bundled with other grants from different funding agencies have a higher impact than non-grant-funded articles. This finding supports the idea of a beneficial effect of competitive grants in science and goes opposite to  Mali et al. (2017)  findings showing that public grants produce impactful research only if researchers' funds come from a unique source. Our study is not exempt from limitations. First, we are forced to use articles published from 2009 to 2013 due to Web of Science and Microsoft Academic Graph data coverage. According to the selected time window, we can trace citations to articles only up to six years after publication.
This time limitation does not allow us to know the articles' citation patters after six years from the publication date. Second, when pairing grant-funded and non-grant-funded articles, we do not control for the manuscripts' content and other unobserved factors influencing the probability of acknowledging an ANR grant. the probability that an ANR grant is acknowledged by an article in Physical sciences. Table  A8  shows that after the Propensity Score Matching, all the average articles' and authors' characteristics are statistically equivalent for grant-funded and non-grant-funded articles at standard significance levels.
research impact? Evidence from France supported by grants. We rely on publication acknowledgments to retrieve funding information and on citation data to assess the articles' impact. We find that articles supported by competitive grants receive more citations than articles not supported by grants in the long run, while the difference is not statistically significant in the short run. We find heterogeneity in the effect of grant funding on citations across fields. , Nice (France), the  Joyez  for their thoughtful comments. This work has been supported by the  French government , through the  UCA   JEDI  and  EUR DS4H Investments  in the Future projects managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-0001  and  ANR-17-EURE-0004. Our paper contributes to the literature by shedding light on the effect of grant funding on the impact of the resulting research for a large European country.
Moreover, it is the first study considering all the articles supported by public funding across research fields and authors' universities. Finally, unlike the previous literature, we apply a probabilistic matching approach to obtain unbiased estimates of the funding effect. To assess the effect of competitive grant funding, we identify the articles supported by grants distributed by the  Agence Nationale de la Recherche (ANR) , the  French  main funding agency. We rely on scientific articles' acknowledgments to distinguish grant-funded articles published between 2009 and 2013 from articles without grant-funding support. Then, we implement a probabilistic matching approach to compare 6,441 ANR grant-funded articles with 6,441 similar non-grantfunded articles (Rubin, 2001;Dehejia and Wahba, 2002).
Results We estimate the impact of grant-funded articles by calculating the difference between the number of citations received by grant-funded articles and non-grant-funded articles. As explained in section 3. 3. , we measure the articles' impact in the short and long run by counting the citations received by the article from t to t+2 and from t+3 to t+5, respectively. We rely on t-tests to estimate the impact differences. T-tests are valid for any distribution in large samples, including highly nonnormal distributions  (Lumley et al. 2002; Tonta and Akbulut, 2020). Table  4 , Column 1, shows that in the short run there is no statistically significant difference between the impact of articles acknowledging ANR grants and those not supported by any grant. On the contrary, Table  4 , Column 2, shows that articles acknowledging ANR grants receive, on average, 0.
580 more citations than articles not supported by any grant in the long run (Grantfunded effect). The value of 0. 580 corresponds to 6. 93% more citations received by grant-funded articles than by non-grant-funded articles (Grant-funded relative effect). We identify 21,381 articles funded by at least one ANR grant and match them with 21,381 similar non-grant-funded articles. On average, the 21,381 grant-funded articles are supported by 3. 46 competitive grants. As in our main analysis, we apply the Propensity Score Matching procedure. For the equation predicting the probability of being grant-funded (Table  F1  in Appendix F), we use the set of covariates reported in Table  1  and pair grant-funded and non-grantfunded articles by selecting the "nearest neighbor. " Table  F2  shows the covariate balance tables pre-and post-matching.
Coarsened Exact Matching The Propensity Score Matching procedure estimates the probability that an article is supported by an ANR grant given the set of articles' and authors' characteristics reported in Table  1. In this robustness check, we implement the Coarsened Exact Matching (CEM) non-parametric procedure  (Azoulay, 2010; Iacus et al. , 2012)  to pair grant-funded publications with non-grant-funded publications. The difference between the Propensity Score Matching procedure applied in our main analysis and CEM is that CEM stratifies articles ex-ante using a non-parametric approach. CEM guarantees the covariate balance ex-ante at the cost of having grant-funded articles that remain unmatched due to the impossibility of finding a non-grant-funded article to pair within the same stratum.
We consider the covariates in Table  1  as the characteristics that allow us to match non-grantfunded articles with grant-funded articles. With the CEM procedure, we coarsen the support of the joint distribution of the covariates into a set of strata. Each article is then allocated into a unique stratum. We drop 212 strata that do not contain at least one grant-funded article and one non-grantfunded article. We end up with 6,229 strata  (6,. Finally, we match each grant-funded article with a non-grant-funded article allocated in the same stratum. If a stratum contains multiple articles, we match a grant-funded article with the most similar non-grant-funded article using the Propensity Score Matching procedure. We end up with a matched sample of 6,229 articles acknowledging ANR grants paired with 6,229 articles not supported by grants.
We identify 1,489 grant-funded articles in Engineering that we match with 1,489 similar non-grant-funded articles drawn from a pool of 15,261 articles in the same field. Table  A5  shows the marginal effects of the equation predicting the probability that an ANR grant is acknowledged by an article in Engineering. Table  A6  shows that after the Propensity Score Matching, all the average articles' and authors' characteristics are statistically equivalent for grant-funded and non-grant-funded articles at standard significance levels. Finally, tables A7 and A8 refer to articles in Physical sciences. We match 3,719 grant-funded articles in Physical sciences with 3,719 similar non-grant-funded articles drawn from a pool of 29,081 articles in the same field. Table  A7  shows the marginal effects of the equation predicting   Life sciences  and Medicine, before (Columns 1  and 2)  and after  (Columns 3  and 4.
Backward citations Q2 Dummy variable that equals one if the number of article's backward citations belongs to the second quartile of the backward citation distribution of our sample of articles in the same publication year. Backward citations Q3 Dummy variable that equals one if the number of article's backward citations belongs to the third quartile of the backward citation distribution of our sample of articles in the same publication year. Articles' and authors' characteristics Variable description Explained variable Grant-funded Dummy variable that equals one if the article reports ANR grants as the unique funding source in the acknowledgments. Explanatory variables Single-author article Dummy variable that equals one if the article has only one author. Multi-author article 2-4 Dummy variable that equals one if the article has two to four authors.
Methodology In this study, quantitative data were obtained from questionnaire responses. Our study was part of a community-based food culture project conducted in eight selected representative school sites in the three provinces (Northern Province, Southern Province and Loyalty Islands) of New Caledonia. The schools were selected on the basis of the following criteria: (1) a representative repartition of the schools between rural and urban areas (respectively, 37% and 63% of the population) and (  2 ) sufficient school size (n > 200). Selected schools were then randomly drawn from among these eligible schools: five in Southern Province (urban), two in Northern Province (rural) and one in Loyalty Islands Province (rural). Ethics approval for this project was obtained from the Consultative Ethics Committee of New Caledonia (CCE 2018-06 001) and the project was conducted in accordance with the requirements of the Declaration of Helsinki.
Data were collected before the Covid-19 epidemic, between July 2018 and April 2019, from 1060 school-going adolescents (11-15 years old) during class time. In each school, two classes per level (6th, 5th, 4th, 3rd) were chosen to respond to the anonymous questionnaire, which consisted of two parts lasting 30 minutes each and was carried out in two stages. In each school, 95% of the expected participants responded due to absences or parental refusal. We removed questionnaires with incomplete responses and obtained 867 completed questionnaires with parental permission. Written consent was obtained from the children's parents before the start of the study. The project was first authorized by the Vice-Rectorate and then presented to the school directors. The targeted schools were contacted, and the project was then proposed to the teaching teams for their acceptance.
To assess diet, a food frequency questionnaire (FFQ) consisting of 28 questions was set up to obtain data on the frequency of food consumption  [31] , but we used only the responses regarding unhealthy food and drinks: foods high in salt, sugar or saturated fat (french fries, processed salted meats, chocolate and confectionery, cakes, pastries and cookies) and beverages that are not recommended. We complied with the Pacific Community (SPC) standards for unhealthy products, which advise a maximum of one unit per day  [30]. In addition to food and drink consumption, additional questions on food purchases on the way to and from school and at the school canteen were included in the questionnaire. The intake frequencies for the individual food items of the FFQ were first converted to daily frequency equivalents (DFEs), calculated by allocating proportional values to the original frequency categories with reference to a base value of 1.