For Diversity Club this week we are going to discuss stereotypes. What are stereotypes, and why do they exist? We have selected a technical paper of modest length to be the focus of this discussion: Susan T. Fiske. Warmth and Competence: Stereotype Content Issues for Clinicians and Researchers. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3801417/
(Since this is running from 12:30 to 1:30, it seems worthwhile to remind you that you can bring your own lunch if you would like to eat while we meet.)
Hi Again All,
This week we are going to discuss bias against women in the workplace. Here is a short reading from the American Bar Association:
I hope to see you there!
Ooh, that looks cool. You could possibly use composite strategies https://hypothesis.readthedocs.org/en/master/data.html#composite-strategies for testing dataframes.
From: Joe A. Wagner
Sent: Thursday, February 25, 2016 11:03 AM
To: Abraham D. Flaxman
Subject: property based testing
Have you seen hypothesis? It looks really useful. I’ve been meaning to incorporate it into my code, but I’m having a hard time defining properties of data frames (which is usually the input of most of my functions).
This long-read from vox.com would be 13 pages if I printed it, but it is for a general audience, so it’s not like a 13 page research paper.
Linked from the link is this amazing video:
I’ve been gifted a steady stream of math clocks over the years, including a really transcendental one that has been in my dining room for quite a while. I didn’t realize how often I used it to check the time until my four-year-old broke the hands off one recent day. (“I wanted to see what happens when you bend them back and forth,” he explained, but I digress.)
The purpose of this blog is to document the *fix* for this failure that we developed together:
“What time is it?”, I inattentively asked myself as a kid came down the stairs this morning. Perfect answer!
This crossed my inbox recently:
The e-book is available now from https://www.manning.com/books/practical-probabilistic-programming. The print version will be available on Friday.
>From the cover:
Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you’ll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you’ll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images.
Short article from the kick-off of a new IHME journal club, with a focus on diversity and health disparities: [link]
Topics that bubbled up in discussion: composition of search committees, pipeline issues and other barriers to attracting diverse candidates, the scale of the problem with systemic racism.