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beauvoir

v0.0.3RubyGems· Ruby

Guess gender by a first name using more detailed, better sourced data from Open Gender Tracker's Global Name Data.<br /> Beauvoir lets you set avg and lower bounds and choose countries from which to draw data. It's important to note that many people identify as neither a men nor a women. It's important, too, to note that many people who do identify as male or female have names that<br /> are held by far more people who identify as another gender. All of these people deserve not to be misgendered in public (or in private). Nevertheless, automatically classifying people by apparent gender can be a very useful tool to perform censuses of communities or publications to detect and quantify perhaps-invisible bias. VIDA is a pioneer in this field, but their "Count" is limited by a manual methodology that depends hundreds of person-hours of labor. There is a place for more automated counts and Beauvoir can help, but if you do a count like this, you should be careful in how you word your findings not to misgender anyone in particular and be responsive to the possibility of errors.

The verdict
Abandoned. Last published 12 years ago. No recent activity — look for a maintained alternative.
No recent activity — look for a maintained alternative.
Live from the RubyGems registry · derived rules, not AI
How it scores
MaintenanceAbandoned
PopularityNiche
SecurityClean
LicensePermissive
DepsZero deps
Maintenance
Last published 12 years ago.
Popularity
45 downloads / week
Security
No known advisories for this version (OSV).
License
MIT
Dependencies
No runtime dependencies
Recent releases
  • 0.0.312 years ago
  • 0.0.2c12 years ago
  • 0.0.2b12 years ago
  • 0.0.1a12 years ago
beauvoir — Guess gender by a first name using more detailed, better sourced data from Open Gender Tracker's Global Name Data.<br /> Beauvoir lets you set avg and lower bounds and choose countries from which to draw data. It's important to note that many people identify as neither a men nor a women. It's important, too, to note that many people who do identify as male or female have names that<br /> are held by far more people who identify as another gender. All of these people deserve not to be misgendered in public (or in private). Nevertheless, automatically classifying people by apparent gender can be a very useful tool to perform censuses of communities or publications to detect and quantify perhaps-invisible bias. VIDA is a pioneer in this field, but their "Count" is limited by a manual methodology that depends hundreds of person-hours of labor. There is a place for more automated counts and Beauvoir can help, but if you do a count like this, you should be careful in how you word your findings not to misgender anyone in particular and be responsive to the possibility of errors. (Ruby / RubyGems) · Modules