Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

Objective: We 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...

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Autor principal:	Paul Sebo
Formato:	article
Lenguaje:	EN
Publicado:	University Library System, University of Pittsburgh 2021
Materias:	accuracy gender determination genderize.io misclassification name name-to-gender performance Bibliography. Library science. Information resources Z Medicine R
Acceso en línea:	https://doaj.org/article/4e0663cfa18448c28fb87b6ae0702cc7
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spelling	oai:doaj.org-article:4e0663cfa18448c28fb87b6ae0702cc72021-11-22T20:41:00ZUsing genderize.io to infer the gender of first names: how to improve the accuracy of the inference1536-50501558-943910.5195/jmla.2021.1252https://doaj.org/article/4e0663cfa18448c28fb87b6ae0702cc72021-11-01T00:00:00Zhttps://jmla.pitt.edu/ojs/jmla/article/view/1252https://doaj.org/toc/1536-5050https://doaj.org/toc/1558-9439Objective: We 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. Methods: We 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). Results: naCoded, 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%). Conclusions: A 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.Paul SeboUniversity Library System, University of Pittsburgharticleaccuracygender determinationgenderize.iomisclassificationnamename-to-genderperformanceBibliography. Library science. Information resourcesZMedicineRENJournal of the Medical Library Association, Vol 109, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	accuracy gender determination genderize.io misclassification name name-to-gender performance Bibliography. Library science. Information resources Z Medicine R
spellingShingle	accuracy gender determination genderize.io misclassification name name-to-gender performance Bibliography. Library science. Information resources Z Medicine R Paul Sebo Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
description	Objective: We 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. Methods: We 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). Results: naCoded, 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%). Conclusions: A 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.
format	article
author	Paul Sebo
author_facet	Paul Sebo
author_sort	Paul Sebo
title	Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
title_short	Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
title_full	Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
title_fullStr	Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
title_full_unstemmed	Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
title_sort	using genderize.io to infer the gender of first names: how to improve the accuracy of the inference
publisher	University Library System, University of Pittsburgh
publishDate	2021
url	https://doaj.org/article/4e0663cfa18448c28fb87b6ae0702cc7
work_keys_str_mv	AT paulsebo usinggenderizeiotoinferthegenderoffirstnameshowtoimprovetheaccuracyoftheinference
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Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

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