bookmarks (hide)296
- Women are dramatically underrepresented in computer science at all levels in academia and account for just 15% of tenure-track faculty. Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals. Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring and the non-independence of hires. Using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America, we investigate the multi-dimensional nature of gender inequality in computer science faculty hiring through a network model of the hiring process. Overall, we find that hiring outcomes are most directly affected by (i) the relative prestige between hiring and placing institutions and (ii) the scholarly productivity of the candidates. After including these, and other features, the addition of gender did not significantly reduce modeling error. However, gender differences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the effects of gender are indirectly incorporated into hiring decisions through gender's covariates. Furthermore, we find evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar efforts by lower ranked departments. These findings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.
- Our neotoma package is part of the ROpenSci network of packages. Wrangling data structures and learning some of the tricks we've implemented wouldn't have been possible without help from them throughout the coding process. Recently Scott Chamberlain posted some code for an R package to interface with ORCiD, the rORCiD package. To digress for a second,…
- Hedonometer.org is an instrument that measures the happiness of large populations in real time. The hedonometer is based on people’s online expressions, capitalizing on data-rich social media, and measures how people present themselves to the outside world.
publications (hide)427
- Radiology, 294 (2): 421--431 (2020)
- (2022)
- IEEE transactions on medical imaging, 33 (2): 577--590 (2013)
- Medical image analysis, (2020)
- Scientific data, 6 (1): 317 (2019)
- Proceedings of the IEEE conference on computer vision and pattern recognition, page 2097--2106. (2017)
- Nature Biomedical Engineering, 6 (12): 1399--1406 (2022)
- ArXiv abs/1711, (2017)
- (2016)cite arxiv:1611.04558.
- AfricaNLP, (2020)
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), page 344--354. New Orleans, Louisiana, Association for Computational Linguistics, (June 2018)
- EMNLP (1), page 1306-1320. Association for Computational Linguistics, (2021)
- (2020)
- International Conference on Learning Representations, (2018)
- (2016)
- Advances in Neural Information Processing Systems, 30, Curran Associates, Inc., (2017)
- Machine Learning for Healthcare Conference, page 913--927. PMLR, (2020)
- AAAI, page 590-597. AAAI Press, (2019)
- (2021)cite arxiv:2112.08191Comment: 4 pages, 2 figures, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) demonstrations track.
- arXiv preprint arXiv:2104.03543, (2021)
