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on Sociology of Economics |
| By: | Alabrese, Eleonora; Capozza, Francesco; Garg, Prashant |
| Abstract: | As social media becomes prominent within academia, we examine its reputational costs for academics. Analyzing Twitter posts from 98, 000 scientists (2016-22), we uncover substantial political expression. Online experiments with 4, 000 U.S. respondents and 135 journalists, rating synthetic academic profiles with different political affiliations, reveal that politically neutral scientists are seen as the most credible. Strikingly, political expressions result in monotonic penalties: Stronger posts more greatly reduce the perceived credibility of scientists and their research and audience engagement, particularly among oppositely aligned respondents. Two surveys with scientists highlight their awareness of penalties, their perceived benefits, and a consensus on limiting political expression outside their expertise. |
| Keywords: | Twitter, Scientists' Credibility, Polarization, Online Experiment |
| JEL: | C93 D72 D83 I23 Z10 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:wzbiii:336443 |
| By: | J. Ignacio Conde-Ruiz; Clara I. González; Miguel Díaz Salazar |
| Abstract: | This paper combines artificial intelligence with economic modeling to design evaluation committees that are both efficient and fair in the presence of gender differences in economic research orientation. We develop a dynamic framework in which research evaluation depends on the thematic similarity between evaluators and researchers. The model shows that while topic balanced committees maximize welfare, this research neutral-gender allocation is dynamically unstable, leading to the persistent dominance of the group initially overrepresented in evaluation committees. Guided by these predictions, we employ unsupervised machine learning to extract research profiles for male and female researchers from articles published in leading economics journals between 2000 and 2025. We characterize optimal balanced committees within this multidimensional latent topic space and introduce the Gender-Topic Alignment Index (GTAI) to measure the alignment between committee expertise and female-prevalent research areas. Our simulations demonstrate that AI-based committee designs closely approximate the welfare-maximizing benchmark. In contrast, traditional headcount-based quotas often fail to achieve balance and may even disadvantage the groups they intend to support. We conclude that AI-based tools can significantly optimize institutional design for editorial boards, tenure committees, and grant panels. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:fda:fdaddt:2026-01 |