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Vokabular: gender-guesser

name dictionary for assigning gender categories to given names; vocabulary of given names with gender classifications

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Basic information

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(P34)?Naming:	gender-guesser
(P1266)?Abbreviation:	—
(P724)?Digest:	The "Gender Guesser Dataset" is a multilingual, controlled vocabulary designed for the automated determination of gender and honorifics based on given names. While its core foundation is built upon the C program "gender.c" and the "nam_dict.txt" text database originally developed by Jörg Michael, the dataset has been continually refined and expanded by the open-source community during its adaptation into modern software environments (such as the Python "gender-guesser" package). The dataset features a flat hierarchy containing more than 45,000 unique first names, which expand to over 70,000 total entries when accounting for country-specific variants. Since many given names indicate different genders depending on the region, the dataset incorporates integrated country and language codes to resolve ambiguities. Classification is executed via a differentiated, six-tier system that captures absolute values ("male", "female") as well as statistical probabilities ("mostly_male", "mostly_female", "andy" for androgynous, and "unknown").
(P887)?URL:	https://pypi.org/project/gender-guesser/, https://github.com/lead-ratings/gender-guesser
(P121)?Genre classification:	Word lists and category systems
(P15)?Medientyp:	Online resource
(P608)?Field of knowledge:	DDC 300: Social sciences
(P936)?Object types:	Given name, sex category
(P984)?Object type properties:	Language, sex category

Number of integrated items

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(P613)?Number of integrated items:	70.075 Standard designation(s)

Contact

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(P845)?Creator:	ByRatings, Jörg Michael
(P518)?Technically realized by:	ByRatings, Jörg Michael
(P206)?Publisher:	ByRatings
(P153)?Postal address:	—
(P722)?Email address:	—

Methodology

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(P280)?Variant:	Data curation, Entity recognition, Quantitative analysis
(P575)?Design features:	Simple list of terms without definitions
(P902)?Methodology:	Based on current technical terms (Applies to a small proportion), Based on source terms (Applies (overall)), Technical terms have a date (Not the case), The source terms have a dating (Not the case), The source terms have georeferences (Not the case), The source terms have references (Not the case), The technical terms have a theoretical concept (Applies (overall)), The technical terms have georeferencing (Not the case)
(P15)?Media type:	Online resource
(P1000)?Organisational structure:	Anyone can make changes
(P999)?Sustainability:	The project is supported by a larger community in the medium term
(P942)?Number of hierarchy levels:	1
(P900)?Completeness:	Open for growth, completeness cannot be achieved

Version

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(P18)?Language:	English
(P1152)?Date of first publication:	2015
(P222)?Date of publication:	2015
(P939)?Versioning:	The work is available in individually designated versions

Cross concordances

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(P898)?External matching:	Not the case
(P573)?Target group:	Citizen Science, Research project, Researcher

Documentation

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(P382)?Specifications:	documentation
(P978)?External tutorial:	—
(P180)?License:	CC BY-SA 4.0

Provision of data

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(P940)?Technology:	GitHub, HTML
(P125)?Status of possesio:	Complete download possible
(P15)?Media type:	Online resource
(P937)?Referencing method:	Internal identifier(s)
(P903)?Data format:	CSV, JSON
(P923)?API endpoint URL:	—
(P941)?SPARQL endpoint URL:	—
(P995)?Services offered:	—
(P950)?Cite as:	gender-guesser (Python library), PyPI – Python Package Index, https://pypi.org/project/gender-guesser/

Dataset editing

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(P1302)?Dataset editing:	Katja Liebing