1. pulls golang contributors list, predicts gender of each first name, prints gender stats
2. pulls gophers slack members list, predicts gender, prints gender stats
3. pulls Go committers from bigquery github archive, predicts gender, prints gender stats

The purpose of this project is to try to give some (any) statistics to track improvement of the M/F gender ratio in the Go community over time.

Please note that heuristic and probabilistic gender classification by first name is horribly imperfect. Please also note that gender is not binary, and it is ultimately up to each individual to determine how they identify.

I took the name => gender data from OpenGenderTracking/globalnamedata

Quote from their announcement blog article on 06/03/2013:

Today, we are releasing Global Name Data, a dataset of birth name-gender mapping which we believe to be the most comprehensive in the world.

$ go get github.com/adams-sarah/go-gender-stats

The classifier program in classifier/classifier.go is stolen from github.com/hstove/gender (thanks @hstove)

You will need to recreate the classifier before running go-gender-stats:

$ cd $GOPATH/src/github.com/adams-sarah/go-gender-stats/classifier
$ go run classifier.go

This will generate a new classifier file at ../classifier.serialized, so be sure you're in the classifier dir when you run.

$ go-gender-stats

Go Contributors by Gender:

  - Female: 5.57%

  - Male: 94.43%

  # Total: 1149

-------------

Slack Gophers by Gender:

  - Female: 6.92%

  - Male: 93.08%

  # Total: 14562

-------------

Github Go Committers by Gender:

  - Female: 5.99%

  - Male: 94.01%

  # Total: 74582
  

Go Contributors by Gender:

  - Female: 5.57%

  - Male: 94.43%

-------------

Slack Gophers by Gender:

  - Female: 6.95%

  - Male: 93.05%

Go Contributors by Gender:

  - Female: 5.18%

  - Male: 94.82%

-------------

Slack Gophers by Gender:

  - Female: 6.39%

  - Male: 93.61%

Go Contributors by Gender:

  - Female: 5.75%

  - Male: 94.25%

  - Unknown: 0.00%

Go Contributors by Gender:

  - Female: 4.40%

  - Male: 84.94%

  - Unknown: 10.66%