11–20 of 162 posts
Same thing with job applications: "In an attempt to increase workplace diversity, the Australian public service implemented a .. trial removing all mention of gender and ethnicity from applications. It was found that removing gender from thousands of applications made women less likely to advance through the recruitment process compared to cases where gender was visible. The trial found that a woman's name on an appl…
Funny thing to see is that as a country becomes MORE developed, women tend to be LESS inclined to attend STEM studies.
Same thing with job applications: "In an attempt to increase workplace diversity, the Australian public service implemented a .. trial removing all mention of gender and ethnicity from applications. It was found that removing gender from thousands of applications made women less likely to advance through the recruitment process compared to cases where gender was visible. The trial found that a woman's name on an appl…
It will get even more India focused because of the latest laws.
Same thing with job applications: "In an attempt to increase workplace diversity, the Australian public service implemented a .. trial removing all mention of gender and ethnicity from applications. It was found that removing gender from thousands of applications made women less likely to advance through the recruitment process compared to cases where gender was visible. The trial found that a woman's name on an appl…
http://www-2.rotman.utoronto.ca/facbios/file/Whitening%20MS%...
https://www.alexandria.unisg.ch/19805/1/Cole%20et%20al.%2020...
https://d1wqtxts1xzle7.cloudfront.net/36436480/Gender_et_bia...
As long as there aren't as much women studying all kinds of engineering, you're going to see disparaties in the workforce. Lets stop trying to force equal distribution but rather equal opportunity. Funny thing to see is that as a country becomes MORE developed, women tend to be LESS inclined to attend STEM studies.
Let “Hollywood” glamorize STEM, propagandize it and you’ll get a better pool of talent. Instead the proposition is to scrape the bottom of the barrel harder.
As long as there aren't as much women studying all kinds of engineering, you're going to see disparaties in the workforce. Lets stop trying to force equal distribution but rather equal opportunity. Funny thing to see is that as a country becomes MORE developed, women tend to be LESS inclined to attend STEM studies.
As long as there aren't as much women studying all kinds of engineering, you're going to see disparaties in the workforce. Lets stop trying to force equal distribution but rather equal opportunity. Funny thing to see is that as a country becomes MORE developed, women tend to be LESS inclined to attend STEM studies.
This is the main problem. It’s hard. We could fix it if we were despotic and put people into tracks like the USSR who turned out lots of talented women in STEM, but were not and Film, TV and YT, etc highlight the easy life of glamour and fame, etc. So you end up with a million wanna be starlets and fewer women in STEM than statistics would have. Let “Hollywood” glamorize STEM, propagandize it and you’ll get a better…
Earlier quoted context omitted.
The whole thing is written from the assumption that investors discriminate against women and nonwhites, and that the purpose of research is to confirm this pre-existing belief, rather than find out the objective truth. > [..] future research on investor biases should focus on networks and in-person interactions. Read: the results contradicted our hypothesis, so rather than update our hypothesis, let's shift the focus…
This is exactly how science should work. You can't perform a study, look at the data and pick a result that suits your bias. You're supposed to suggest a hypothesis, and conduct your experiment. If the results contradict your hypothesis, it doesn't mean the opposite of your hypothesis is true, simply that your hypothrsis was false, and that's ok. It's also more impressive that they have simply published their results…
"Have you seen the fire sir?" "I have only seen smoke" "But have you seen the fire?" "It's irrelevant because I was only looking for smoke!".
I mean I know they teach it in naive introductory experiment design in some soft science departments but c'mon it's neither smart nor correct. If you need a separate experiments for everything because otherwise you will run into "one of the 100 hypothesis is bound to be correct" problem then your priors are too weak, you're relying on getting lucky anyway and just end up discarding useful datapoints.
If it's 5 out of 100 hypothesis fitting your data or 5 hypothesis out of 100 experiments passing "statistical significance" check doesn't matter at the end of the day. Both lead to shaky conclusions. The data being relevant depends on the experiment design (how it's collected). It doesn't depend on what you thought it's relevant for beforehand.
"After long and thoughtful consideration, we discarded the idea of assigning African-American names to our fictitious entrepreneurs. First, there has been a concern about the ability to disentangle race and socioeconomic perception by using distinctly African-American names (Fryer and Levitt (2004)). Second, and most importantly, African-American entrepreneurs are woefully underrepresented in high-impact entrepreneurship (less than 1%, see Gompers and Wang (2017)). This poses a substantial challenge for our setup, because even names that are disproportionately likely to be African-American in the general population (such as “Martin Jackson”) may not be perceived as African-American by investors in this context."
It's an interesting point, are there any unambiguously African-American names? I wouldn't have guessed Martin Jackson was African-American, but I'm English.
Earlier quoted context omitted.
>Our results suggest that investors do not discriminate against female or Asian entrepreneurs when evaluating unsolicited pitch emails... Are they implying that investors do discriminate against non-Asian males? Why write it the other way around.
From omission it seems they reply less to black and white men. But these groups are large, however, sadly I don’t think it’d be easy to test on feigned education/class, so we won’t know what causes the bias.