Editor's note: I'm not the only one who has intriguing things to share. So from time to time, I'll pass the conch shell to guest editors for one edition. Our first guest eds are the co-founders of Model View Culture, Shanley Kane and Amelia Greenhall. "Model View Culture is a new independent media company covering technology, culture and diversity," they write. "It aims to provide a platform for social and cultural critique within tech, build critical thinking and consciousness in the community, and highlight diverse writers, technologists, authors, artists and intellectuals — people working either outside of the mainstream tech establishment, or working within it to make it better."
Kane and Greenhall picked five intriguing things that speak to the themes of their publication, which launched yesterday.
"Corners of the world where women have yet to tread. Shine a light."
"I have developed a small program that produces the probability distribution of gender selection for tech events in a gist found here. Included with this is a simulation experiment that shows just how unlikely the situation that resulted in this conference really is. This simulation should affirm the mathematics as well."
+ Twee-Q — a Twitter tool that shows you the gender breakdown of the people you RT - men are generally RTed much more often than women.
"Nonetheless, QS, like Occupy before it, represents a decentralized challenge to traditional authority that is trying to be everything to everyone while still speaking for no one. Whereas feminists famously declared a generation ago that 'the personal is political'—a shorthand call to collectivism, recognizing the struggles individual women face are tied systems of oppression that cannot be fought individually—Occupy made the political personalizable. It had politics, but no unified message other than, perhaps, 'shit is fucked up and bullshit.' Occupy was more like Twitter or Tumblr taken to the streets—a platform where you could express your personal discontent alongside a multitude of others expressing their personal discontent. It refused to release an overarching list of demands, unwilling to lose some of “us” by becoming a more coherent Us.
At present, Quantified Self is similarly amorphous. The Show&Tell format deliberately focuses on personal stories: what I did, how I did it, what I learned. The 'start your own Show&Tell' FAQ follows a similar format: Here’s what we’ve done, but if you want to call yourself 'Quantified Self' and do something else, that’s fine too. Quantification might be central, but self-determination is paramount. For a while, making 'the self' the center of Quantified Self seemed to pre-empt conflict between QSers; if no one speaks for anyone else, then no one has anyone to argue with."
with regard to the technical roles of women.
"Another 7-year-old in attendance that day was Jayda Ostrum, who traveled to the event from Philadelphia. Like Landeau, Ostrum has taken computer programming classes before; she once took part in a robot-building competition but said she prefers coding.
'I don’t like rushing,' she said, adding that the robot competition had a deadline, whereas constructing codes allows her to save a project and return to it later. 'I just love the whole program.'"
Today's 1957 English Language Tip
alien. The prepositions after the adj. are from & to. There is perhaps a slight preference for from where mere difference or separation is meant (We are entangling ourselves in matter alien from our subject), & for to when repugnance is suggested (cruelty is alien to his nature). But this distinction is usually difficult to apply, & the truth seems rather that to is getting the upper hand of from in all senses.
Update: Google did not drop my newsletter into the spam chute, according to yesterday's open rate. Whew. Rusty Foster suggested a plausible theory: I linked to some Bitcoin stuff, which the Googles find suspicious. (And in recompense for this idea, you should subscribe to his newsletter, Today in Tabs.)
Data scientist Hilary Mason wrote to say there is a technical term for the ability to grok an algorithm's behavior:
"In the machine learning community, the ability to understand why an algorithm behaves the way it does is called "interpretability." It's actually really important to consider in the design of algorithms that are potentially used to make decisions for which one can be legally liable."