But who is going to call out OUR OWN FIELD for trumping up this sort of nonsense? And by "nonsense" I mean "systematically ignoring a great deal of evidence that contests your preferred narrative."
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Stereo acc work is no more immune to criticism than any other area. But there is a world of difference between critically grappling w/work contesting your preferred narrative, & simply ignoring. Ignoring large bodies of lit that contest your conclusions should not be an option.
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Nothing in this thread addresses policy or ethics. Empirical evid. of accuracy in any stereotype says EXACTLY ZERO about whether or how to go about diversity and inclusion, affirmative action, or ANYTHING ELSE. It is just the damn data.
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An accurate stereotype does not justify discrimination, harassment, or mistreatment of any type, just as knowing that NJ is warmer than Alaska (in general) does not mean it would be a good idea for me to walk outside shirtless in shorts today.
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Replying to @PsychRabble
However, the rather high accuracy of them does force a purely rational agent to take those stereotypes into account, even when dealing with individuals https://emilkirkegaard.dk/en/?p=5788 . So it is a little bit thornier than being "just data".
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Replying to @DM_Berger @PsychRabble
Agreed. It's not that easy. Ignoring diagnostic information?
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Replying to @MorHeene @PsychRabble
Esp. since it has actual consequences. E.g. doing the "right" thing and ignoring the information will make you worse at predicting people's behaviors and at correctly understanding the world. It doesn't feel right to me to demand people act in ways that are harmful to them.
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Replying to @DM_Berger @MorHeene
This is also more complex than it seems, prob too much for twitter. Short version: 1. For important things, it behooves decision makers that to max out diagnostic individuating info; 2. Evid of strong stereo acc ≠you can/should assume your stereos are always right.
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Replying to @PsychRabble @MorHeene
Absolutely. Stereotypes are useful because they give decent, first-pass predictions from obvious (usually visual) indicators (e.g. race, sex). But there are things that are less obvious and far more predictive (e.g. IQ, class, beliefs) that should get much more weight.
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To solve the first-pass obviousness issue, we need to find variables that are quick and obvious to spot and more predictive than things like race and sex, or, alternately, find visual / obvious indicators for the variables that are more predictive (e.g. verbosity -> IQ).
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And therein lies the problem. The most important variable is intelligence, but group differences in it are verboten topic because reasons, so generally ignored in polite company. So, people move to the next most important predictors, education, which has tons of issues.
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