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on Sociology of Economics |
| By: | RePEc, IDEAS |
| Abstract: | The data presented here are experimental. They are based on a sample of the research output in Economics and Finance. Only material catalogued in RePEc is considered. For any citation based criterion, only works that could be parsed by the CitEc project are considered. For any ranking of people, only those registered with the RePEc Author Service can be taken into account. And for rankings of institutions, only those listed in EDIRC and claimed as affiliation by the respective, registered authors can be measured. Thus, this list is by no means based on a complete sample. You can help making this more comprehensive by encouraging more publications to be listed and more authors to register. |
| Date: | 2023–09–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:b6kcr&r=sog |
| By: | Piera Bello; Alessandra Casarico; Debora Nozza |
| Abstract: | We investigate the extent to which research similarity between senior and junior researchers influences promotion in academia and study its implications in terms of gender diversity among faculty. Using data on the universe of job applications for tenure-track assistant professor positions in economics in Italy and exploiting NLP techniques (i.e., document embeddings) on the abstract of each publication of the scholars in our dataset, we propose a novel measure of research similarity, which can capture closeness in research topics, methodologies or policy relevance between candidates and members of selection committees. We show that the level of similarity is strongly associated with the winning probability. Moreover, while there are no gender differences in average similarity, maximum similarity with members of the selection committee is lower for female candidates. This gender gap disappears when similarity is calculated only focusing on female members of the committee. The results suggest that similarity bias in male-dominated environments can have implications for gender and research diversity. |
| Keywords: | cosine similarity, document embeddings, academia, economics, gender differences, labour force composition |
| JEL: | J16 J71 J82 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10657&r=sog |
| By: | Sam Arts; Nicola Melluso; Reinhilde Veugelers |
| Abstract: | We use text mining to identify the origin and impact of new scientific ideas in the population of scientific papers from Microsoft Academic Graph (MAG). We validate the new techniques and their improvement over the traditional metrics based on citations. First, we collect scientific papers linked to Nobel prizes. These papers arguably introduced fundamentally new scientific ideas with a major impact on scientific progress. Second, we identify literature review papers which typically summarize prior scientific findings rather than pioneer new scientific insights. Finally, we illustrate that papers pioneering new scientific ideas are more likely to become highly cited. Our findings support the use of text mining both to measure novel scientific ideas at the time of publication and to measure the impact of these new ideas on later scientific work. Moreover, the results illustrate the significant improvement of the new text metrics over the traditional metrics based on paper citations. We provide open access to code and data for all scientific papers in MAG up to December 2020. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.16437&r=sog |
| By: | Laxdal, Aron Gauti; Haugen, Tommy |
| Abstract: | • The peer review system is viewed by many as being flawed and antiquated. • While it’s unlikely that the system will be overhauled completely, some changes seem to be warranted. • Our proposal is to change the incentives to do peer review by making reviews a part of the tenure criteria and increasing transparency throughout the process. • We believe these changes would not only increase willingness to review, but also lead to shorter turnaround times and increase the quality of reviews. |
| Date: | 2023–09–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:7c5xr&r=sog |
| By: | Christian Lessmann; Ali Sina Önder |
| Abstract: | We analyze whether the social media popularity of Twitter star scientists, who were identified by Science in a 2014 report, pays off in terms of an increased number of citations. To establish a causal relationship, we use the COVID-19 global pandemic as a quasi-natural experiment exogenously increasing public attention and the demand for expertise. Using Twitter science stars’ and their coauthors’ publications on COVID related topics prior to the break out of the pandemic, we run a difference-in-differences analysis for annual incoming citations of the two groups. We find that the Twitter star status added about 1.07 extra citations following the breakout of COVID-19 per year per article, corresponding to about 70% of the already existing citation gap between Twitter science stars and their coauthors. Moreover, we also document that the publication of the Science list on Twitter science stars in 2014 per se caused an increase in citations, i.e. the publication of the supposed celebrity status by Science already benefited the stars, which meant 1.06 more citations per year per article compared to their coauthors. Treatment based on scientists’ Kardashian indexes yields no robust effects, implying that unjustified social media popularity does not pay off in terms of citations. |
| Keywords: | social media, expertise, Kardashian index, citations, Covid |
| JEL: | J24 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10661&r=sog |