Quartz: With a series of Wikipedia Edit-a-Thons, Indian women are finally getting their due online

Quartz: With a series of Wikipedia Edit-a-Thons, Indian women are finally getting their due online. “You only have to look at the Wikipedia page of the early 20th century Indian painter Amrita Sher-Gil to know that she’s a household name: It’s detailed, well-sourced, and full of pictures of her works, some of which have been sold for millions of dollars. But there’s a whole world of contemporary woman artists that hasn’t been half as lucky when it comes to Wikipedia, lacking even a page on the go-to online encyclopedia, let alone a comprehensive one. And it’s this glaring discrepancy that a small group of students and young professionals came together to fix on Sept. 16, as part of a Wikipedia Edit-a-Thon dedicated to Indian women in contemporary art.”

Lifehacker: Where to Find More Diverse Stock Photos

Lifehacker: Where to Find More Diverse Stock Photos. “It’s easy to find stock photos of slim white people doing stereotypical activities—women laughing alone with salad and that sort of thing. If that’s not what you’re looking for, may we suggest some of these sites that break the mold?” Only three sites unfortunately, but two of them I’d never heard of. Hopefully the Lifehacker comments will add more suggestions.

Los Angeles Times: Google faces class-action suit alleging gender pay discrimination

Los Angeles Times: Google faces class-action suit alleging gender pay discrimination. “The suit, filed Thursday in San Francisco Superior Court, follows a federal labor investigation that made a preliminary finding of systemic pay discrimination among the 21,000 employees at Google’s headquarters in Mountain View, Calif. The initial stages of the review found women earned less than men in nearly every job classification.”

Arizona State University: Research team discovers that cooperation, competition are different motivators

Arizona State University, which I must say is doing some interesting research: Research team discovers that cooperation, competition are different motivators. “China’s largest recipe-sharing platform needed a carrot to motivate more content from users, and research from a team of Arizona State University professors was able to pinpoint what works. A new study by faculty in the W. P. Carey School of Business found that specific kinds of notifications could elicit more content from the app users — and that there are differences between men and women. Feedback that promoted a message of helping others prompted women to contribute more content, while men were more likely to respond to competitive messages.” Oh boy, the headline and the excerpt make this sound boring. It isn’t. Go read.

Gizmodo: Google Employees Organise Their Own Study Of Gender Pay Gap

Gizmodo: Google Employees Organise Their Own Study Of Gender Pay Gap. “In April, the US Department of Labor accused Google of gender pay discrimination. The tech behemoth denied the allegations, and when the DoL requested historical salary records from the company, Google argued that the endeavour was too expensive. Lucky for Google, good samaritans at the company have led efforts to compile the wage data.”

PRNewswire: Newly Completed Titles Available from Accessible Archives

PRNewswire: Newly Completed Titles Available from Accessible Archives (PRESS RELEASE). “Accessible Archives, Inc.®, an electronic publisher of full-text primary source historical databases, has announced the completion of additional titles in its African American Newspapers and Women’s Suffrage collections. The five newspapers are now fully imaged, with the XML TEI Lite DTD utilized to re-key each article at the highest accuracy level, resulting in optimum search results and clean text. MARC records also are included.”

Wired: Machines Taught by Photos Learn a Sexist View of Women

Wired: Machines Taught by Photos Learn a Sexist View of Women. “LAST FALL, UNIVERSITY of Virginia computer-science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. ‘It would see a picture of a kitchen and more often than not associate it with women, not men,’ he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to ‘train’ image-recognition software.”