On Intersectionality: How Complex Patterns of Discrimination Can Emerge From Simple Stereotypes
Bibliographic record
Abstract
Patterns of discrimination are often complex (i.e., multiplicative), with different identities combining to yield especially potent discrimination. For example, Black men are disproportionately stopped by police to a degree that cannot be explained by the simple (i.e., additive) effects of being Black and being male. Researchers often posit corresponding mental representations (e.g., intersectional stereotypes for Black men) to account for these complex outcomes. We suggest that complex discrimination can be explained by simple stereotypes combined with threshold models of behavior—for example, “if someone’s threat level seems higher than X, stop that person.” Simulations provide proof of this concept. We show how gender-by-race discrimination in both promotions and police stops can be explained by simple stereotypes. We also explore race-by-age discrimination in police stops, in which racial disparities are greater for young adolescents. This work suggests that complex behaviors can sometimes arise from relatively simple cognitions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".