ArticleJournal of the Medical Library Association : JMLA2021
Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference.
Article in Journal of the Medical Library Association : JMLA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sex and gender considerations in randomized controlled trials in critical care nephrology: a meta-epidemiologic study.BMC medicine · 2025Pooled it
- Progress and Gaps: Two Decades of Female Authorship in High-Impact Otolaryngology Journals.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- Reaching gender parity and improving internationalisation after five decades of FSBI symposia, but subtle career-stage effects on timekeeping remain.Journal of fish biology · 2026Article
- Trends in Women Authoring Editorials in Cardiothoracic Surgery Journals.Brazilian journal of cardiovascular surgery · 2026Article
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- Gender Equity Issues in Orthopaedics: A Scoping Review.Indian journal of orthopaedics · 2025Review
- Gender differences in collaboration and career progression in physics.Royal Society open science · 2025Article
- Twenty years of infectious disease dynamics research: gender and race imbalances in publication and citation practices.Proceedings. Biological sciences · 2025Article
- Representation of women on National Institutes of Health study sections before and during COVID-19 pandemic.Journal of clinical and translational science · 2025Article
- Gender and Authorship in Annals of Surgery: A nineteen-year review including the pandemic.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Article
- Inferring gender from first names: Comparing the accuracy of Genderize, Gender API, and the gender R package on authors of diverse nationality.PLOS digital health · 2024Article
- Article
- Systematic Review of Women Leading and Participating in Nephrology Randomized Clinical Trials.Kidney international reports · 2024Article
- Comparative analysis of automatic gender detection from names: evaluating the stability and performance of ChatGPTPeerJ. Computer science · 2024Article
- Name-based demographic inference and the unequal distribution of misrecognition.Nature human behaviour · 2023Article
- COVID-19 retracted publications on retraction watch: A systematic survey of their pre-prints and citations.Heliyon · 2023Article
- How well does NamSor perform in predicting the country of origin and ethnicity of individuals based on their first and last names?PloS one · 2023Article
- Gender Differences in National Institutes of Health Grant Submissions Before and During the COVID-19 Pandemic.Journal of women's health (2002) · 2022Article
- "Broadcast your gender." A comparison of four text-based classification methods of German YouTube channels.Frontiers in big data · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe recently showed that genderize.io is not a sufficiently powerful gender detection tool due to a large number of nonclassifications. In the present study, we aimed to assess whether the accuracy of inference by genderize.io can be improved by manipulating the first names in the database.
methodsWe used a database containing the first names, surnames, and gender of 6,131 physicians practicing in a multicultural country (Switzerland). We uploaded the original CSV file (file #1), the file obtained after removing all diacritic marks, such as accents and cedilla (file #2), and the file obtained after removing all diacritic marks and retaining only the first term of the compound first names (file #3). For each file, we computed three performance metrics: proportion of misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded).
resultsnaCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%).
conclusionsA relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.