Coding Like a Girl
summary
Argues that measurable diversity effects appear in data not platitudes citing a bug that affects 60% of customers and is fixed after eight hours debugging, Project Implicit bias timings (roughly 26–28% associate men with science), corporate diversity reports from Google Apple Facebook and mentoring programs on freenode, and studies showing groups with at least one woman raise collective intelligence and mixed‑gender papers get ~32% more citations and boards with women correlate with up to 35% higher returns.
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chapters (29)
- 0:00Intro
- 1:17About me
- 2:11What to expect
- 2:53Diversity
- 3:37Types of Diversity
- 4:36Bias
- 6:38Project Simplicity
- 7:22Test Data
- 7:38Test Results
- 8:59Why I like a girl
- 10:48Women cant fail
- 14:28Culture
- 17:09Gender
- 17:27Yahoo
- 20:22Facebook
- 23:09Why women dont get promoted
- 31:27Why women leave companies
- 33:46Why diversity matters
- 35:05How they did that
- 36:03Social Sensitivity
- 38:00Innovation
- 38:46Competitive Advantage
- 39:23Thinking Differently
- 39:59More Women
- 41:21Cosmos
- 41:35Black Men in Science
- 43:05Generic Differences
- 43:36Improving Diversity
- 46:11Mentoring
