The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Gender Prediction Methods Based on First Names with genderizeR
Open accessAbstract
In recent years, there has been increased interest in methods for gender prediction based on first names that employ various open data sources.These methods have applications from bibliometric studies to customizing commercial offers for web users.Analysis of gender disparities in science based on such methods are published in the most prestigious journals, although they could be improved by choosing the most suited prediction method with optimal parameters and performing validation studies using the best data source for a given purpose.There is also a need to monitor and report how well a given prediction method works in comparison to others.In this paper, the author recommends a set of tools (including one dedicated to gender prediction, the R package called genderizeR), data sources (including the genderize.ioAPI), and metrics that could be fully reproduced and tested in order to choose the optimal approach suitable for different gender analyses.
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Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 2×
“That is why we use the Brier Score, which originated from a verification system of weather forecasts (Brier, 1950) and is known as a more proper classification accuracy score for predictions based on probabilities.”
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Cited by
The preeminence of ethnic diversity in scientific collaboration
AlShebli, B. K., Rahwan, T., & Woon, W. L. (2018) · Nature Communications · doi:10.1038/s41467-018-07634-8 · Cited by 490
Comparison and benchmark of name-to-gender inference services
Santamaría, L., & Mihaljević, H. (2018) · PeerJ Computer Science · doi:10.7717/peerj-cs.156 · Cited by 401
Gender Disparity in Citations in High-Impact Journal Articles
Chatterjee, P., & Werner, R. M. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.14509 · Cited by 367
Men Set Their Own Cites High: Gender and Self-citation across Fields and over Time
King, M. M., Bergstrom, C. T., Correll, S. J., Jacquet, J., & West, J. D. (2017) · Socius: Sociological Research for a Dynamic World · doi:10.1177/2378023117738903 · Cited by 275
AlShebli, B., Makovi, K., & Rahwan, T. (2020) · Nature Communications · doi:10.1038/s41467-020-20617-y · Cited by 23
Retracted
Metadata
- Authors
- Wais, K.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2016
- Volume
- 8
- Issue
- 1
- Pages
- 17
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2785912406
- Cited by
- 197
- References
- 1
- FWCI
- 5.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
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