“Invisible” Black Women Being Denied, Passed Over, and Ignored as a Function of Racism (not Sexism) Among White People
Bibliographic record
Abstract
There is considerable debate in psychology about the extent to which Black women (vs. Black men or White women) are targeted for discrimination, especially as a function of racism/sexism. To gain greater insight into the perpetration of racial and gender-based discrimination against Black women, Study 1 (N = 431; White MTurk workers) considered whether individual endorsement of sexism/racism moderates healthcare discrimination against Black or East-Asian (vs. White) women. Participants completed measures of modern racism/hostile sexism before being randomly assigned to make healthcare recommendations regarding a Black, White, or East-Asian female target. Collapsing across individual differences, there was not significantly more opposition to recommending healthcare resources for Black or East-Asian (vs. White) women. However, COVID-19 and general physical-health discrimination against Black (vs. White) women significantly increased as individual endorsement of racism increased. Furthermore, participants higher (vs. lower) in endorsement of racism were more opposed to recommending healthcare resources for Black (but not for White) women. Individual differences did not moderate any form of healthcare discrimination against East-Asian (vs. White) women. \n Study 2 (N = 480; White male MTurk workers) considered whether individual endorsement of sexism or racism moderated STEM-workforce discrimination against Black women (vs. Black men or White women). Participants completed prejudice measures before being randomly assigned to make hiring and promotion timeline recommendations for a Black female, Black male, White female, or White male target. Collapsing across individual differences, Black women (vs. White women or Black men) were not deemed less hirable or needing longer promotion timelines. Additionally, individual differences in racism did not significantly moderate STEM-workforce discrimination against Black (vs. White) women, but a marginally significant trend revealed more hiring opposition against Black women as racism increased. However, STEM-workforce discrimination against Black women (vs. Black men) was greater among participants higher (vs. lower) in endorsement of racism but not sexism. Furthermore, participants higher (vs. lower) in endorsement of racism were more opposed to hiring Black women (but not Black men or White women) and recommended longer promotion timelines for Black women (but not for Black men). This thesis concludes with a discussion of theoretical implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".