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THIS.
Bias is there - if all people want to argue is âthat number isnât of big enough statistical importance to me,â rest assured it really is to every single trans person out there, regardless of their career path.
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Iâve contributed to this project, and I hope you will, too - shining a light on bad behaviour is key to rooting it out, especially if they are profiting from this bad behaviour. Following the money is a necessary and critical step to combating bigotry everywhere, and this project will help to do just that.
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This sounds like a worthwhile project @AndreaJames . Iâll be tossing a contribution into the hat.
Question: I understand why youâre focusing on anti-trans bias as a priority and why it provides a good basis for developing the analysis framework, but can it be re-purposed in the future to measure and identify other bias patterns and bad actors in the media-industrial complex?
Even when said unfriendly institution claims to be an equal-opportunity one and claims the accusations of unfriendliness are unfair? Stats have been used in the past to demonstrate the reality of those situations (e.g. in terms of hiring, management, admissions, promotions, compensation, etc.) and effect change.
I donât know if they meant it this way, but those are the kinds of weasel words that some outlets use to cover up the underlying bigotry of their stories.
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can it be re-purposed in the future to measure and identify other bias patterns and bad actors in the media-industrial complex?
Yes, that is the plan! A lot of people are dancing around this right now. Most big tech firms are looking at quantifying individual articles with algorithms, but that is beyond my pay grade and extremely hard given language subtleties.
This looks at networks. I believe you can measure bias by looking at âlogrolling,â which exposes networks and is how shady academics game the h-index. You can also tell a lot about its bias by who likes it. This is very important for âdogwhistleâ journalism.
I believe this method when refined by others can show similar patterns.
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Absolutely. Whatâs fascinated me recently is how people with respectable credentials (like that premier JAQ-off, Jordaddy) or people who position themselves as being above reproach (e.g. Ben âhow can I be a fascist? Iâm Jewish!â Shapiro) or just plain Useful Idiots can serve as social media and traditional media gateways to and amplifiers for the hardcore extreme right.
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Yes, thatâs why The Atlanticâs cover story last year is such a good case study. A couple of âtrans-unawareâ friends who read it did not see why trans people as a network were so upset because it seemed âbalanced.â Thatâs when I started thinking hard about how to demonstrate its bias visually. And to be clear, this model also demonstrates pro-trans bias, so if the majority of trans people as a network approve of a piece, that can be shown as well.
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Philosophical Question here, and not trying to Peterson the place up, but this comes up a lot in my inquiry process:
Do trans journalists want to be identified/identify as trans, or do they want to be identified as the gender-alignment theyâve chosen? And if the Atlantic hires a trans writer, is that writer obligated to publicly announce themselves as transgender, or can they just be who they are without having to publicly claim it as a victory for representation? Similarly, when people say there arenât enough parts for Trans actors, is the suggestion a) That non-trans actors (in the gender-neutral form of the noun) are playing trans characters that trans actors would prefer to play or b) That trans actors would like to be audition/ be cast in roles based on the gender-alignment theyâve chosen?
Again, Iâm NOT TRYING to be foolishly, clueless provocative or obtuse, and if I give/gave offense I assure you itâs not deliberate; Iâm actually curious about the philosophical/thought process.
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I was heartened to learn recently of trans actress Nicole Maines portraying televisionâs first trans superhero Dreamer a.k.a. Nia Nal in season 4 of the Supergirl series. Iâm watching season 4 on a well-known (legitimate) streaming service now. Her character, Dreamer, in her civilian job as a reporter, encourages the managing editor at a fictional media company in the show to take an explicit editorial position against nativism in the storyline of season 4, episode 2, coming out to him as a trans person, and explaining how to her, the marginalization and hate toward (in the showâs case literal aliens from space) has extra meaning to her, and he decides to change his editorial approach at the media conglomerate CatCo as a result.
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Iâll start with actors. The Atlantic magazine is like a major film studio. Atlantic.com is like their indie film/TV subsidiary. Last year there were not only zero trans actors in major studio films, there were zero trans characters. Source: http://www.newnownext.com/glaad-studio-responsibility-index-report-2019/05/2019/ On lower levels of the industry, itâs a Catch-22: trans actors donât get to play non-trans actors, but until recently, we didnât even get to play ourselves. Most trans actors would love to be judged on their abilities, but we are far from that point in elite filmmaking. Same thing in elite journalism, as evidenced by the deliberate exclusion of trans journalists from discussion groups.
It would be great to be a trans journalist who did not cover trans issues. That is starting to happen finally, but it took trans people like Janet Mock who didnât have to come out but came out anyway for that to happen.
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This is a great project. I donât know how many times I try to explain to cis folks that a lot of the media coverage on trans issues is biased to an insulting degree, and they donât actually get it.
Getting good data, and trying to make it easily visualized really makes things easier. My wife and I are both trans, and we end up having to swat down bad articles like that crap Atlantic article whenever they come out because we are openly transgender and folks literally ask us. At least that gives us a chance to educate. Itâs the folks that just read that garbage and take it as gospel that are the problem.
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There will always be some people that give a shit. I think raising the issue (I interpreted no negative intent in the original comment - I may be wrong) is fair and not acquiescing to those that will object no matter what.
Given that @AndreaJames is going for a data driven analysis I think it is important to have assumptions and possible weaknesses raised and assessed so that the final conclusions are more robust.
I DO give a shit about trans rights. I donât give a shit about nitpicking numbers in order to gloss over the real world problems that trans folks face in the work place or anywhere else. At the end of the day, it doesnât matter if trans people make up a statistically significant number of people in the world place, because the point is that whether itâs a tiny part of the population, or half the population, they are facing discrimination for no other reasons than bigots are uncomfortably with their own gender identity.
I was more talking about saying that the discrimination that trans people face is statistically insignificant (which was what I was reading his comment as), so we shouldnât do anything or worry about it. Which is what I read from that comment. Who gives a shit if itâs only one person or a million who are facing discrimination, we should all CARE about that one person or those million people, because itâs the RIGHT thing to do. Full stop.
I think that @AndreaJames is doing is great, in fact, and is likely to give those of us who care about this issue ammo. But the converse is true too. I think this kind of work needs to be added to the more social, cultural, and emotional work of working for equality rather than replacing those other things.
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Fine: all trans people are discriminated against. That is true. No need for any further work to be done quantifying it.
If you want solid ammo from a data intensive analysis then numbers will be nit picked over. That is in the nature of looking at data. The responses have already pointed out that for many fields a p value of .05 is the arbitrary yet consensus threshold. If that cannot be met with the data at the Atlantic it does not mean that trans people have not worked there or been discriminated against. It may mean that using those numbers that way are only useful to illustrate a point much as anecdotal evidence is good at connecting to people but not good for making decisions. I will let @AndreaJames figure out the design of her own research and sample sizes.
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Again, Iâm not saying that we shouldnât quantify, but that people who oppose trans rights will employ quantification too, to argue that because trans people are statistically small part of the population we shouldnât pass âspecialâ legislation for them⌠We should.
True. Thatâs why itâs not a silver bullet. The social and cultural matter here, too, because we are talking about people. People do like data, but relying only on data has itâs pitfalls. Iâm arguing for using all of it.
Iâm not arguing against Andreaâs work, but about the people who are saying that because itâs not a larger number of people, nothing should be done or that itâs on trans people to contort themselves into a configuration that makes cisgendered people feel comfortable (mostly by being invisible), because fuck that.
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LET?
Thatâs nice.
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Ok poorly worded. It is her project and her design. There are issues to consider and the wording I used implies granting permission rather than âit is her business and it is a huge project and I am glad I get to stand aside and watchâ
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I donât want to repeat my entire post above, but I explained why the analysis that leads to the p > .05 conclusion is flawed. When discrimination exists in hiring companies that do not discriminate will have more than their âfair shareâ of hires from the targeted group because qualified candidates will be overlooked by others, and so it become less likely that a company would randomly end up with none without discrimination.
Entirely besides that, the setting of p thresholds is arbitrary and is political rather than mathematical. There is no cut-off line where evidence is good or bad, all evidence exists on a spectrum from weak to strong. The zero hires was presented as one piece of evidence among many, not as a way to definitively conclude anything.
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