Let's get more women into computer science
summary
University launches Genie initiative with 300 million kronor to raise female faculty to 40% in ten years, targeting a pipeline that had just 7% female entrants in 2016. Measures include gender-disaggregated data, bias observers on hiring panels, mindfulness groups, school camps and targeted top-up hires; early wins: a recruitment round yields 19 new assistant professors of whom 13 are women after five funded slots, and a girls’ camp materially increases applications. Argument centers on gender schemas and reaching critical mass rather than “fixing women.”
AI-written summary · about
chapters (50)
- 0:00Intro
- 1:15Trinity College Dublin
- 3:17Oxford
- 4:38Edinburgh
- 5:06Swedish universities
- 6:33Academic staff
- 7:50Female students
- 8:57UK statistics
- 9:38European statistics
- 12:03Civil engineering statistics
- 13:08Computer science statistics
- 14:50Gender equality
- 18:15An Inclusive Academy
- 19:28Gender Schemas
- 19:50What are they
- 20:57Unconscious biases
- 22:38Nepotism in peer review
- 24:47Research intensive universities
- 25:28All fields are not terrible
- 25:56Innate talent
- 26:59Meritocracy
- 28:09Two problems
- 28:58Water break
- 29:38Companies know why
- 30:09Survey paper
- 30:35Observations
- 32:22Critical mass
- 33:04European Commission
- 34:48Chalmers
- 36:37Engineering
- 36:47We need to feed the pipeline
- 37:42What have we done with the money
- 38:21New Assistant Professors
- 38:54Possible Interventions
- 39:51Recruitment
- 40:43Gender bias observers
- 41:22Chalmers open call
- 42:07Culture change
- 42:53Mindfulness course
- 43:35Be patient
- 43:48York Chemistry
- 44:29Resistance
- 45:16Future work
- 46:10Encouragement
- 48:03Changing University
- 48:34We need help
- 49:32Listen to the women
- 49:44Women in tech demands
- 51:27Universities for everyone
- 52:07Help
related talks
46mHelp! I Need More Women
51mChampioning women in Tech
51mKeynote: Gender diversity in engineering workplace
24mPanel: Together we can make a change - female LED initiatives
45mWomen in software engineering: what gives?
30mThe Missing 78%: How Women in AI & Data Can Complete the Future of Innovation
31mWomen In Data Panel Debate
31mGender Bias in Tech: Examining Evolution & Persistence of Stereotypes