Sexist Beliefs in a Sexist World: Exploring the Causal Role of Sexism in Sexist Beliefs
Bibliographic record
Abstract
Abstract The claim that prejudice causes prejudiced beliefs is a familiar one. Call it the causal claim. In this paper, I turn to sexism and sexist beliefs to explore the causal claim within the context of current debates in the ethics of beliefs about moral encroachment on epistemic rationality. My goal is to consider and arbitrate between plausible ways of fleshing out the idea that the non-doxastic dimensions of sexism (including its motivational and affective components as well as its structural and institutional varieties) cause sexist beliefs in a normatively significant way – that is, in a way that can render those beliefs epistemically deficient. I suggest that, in conjunction with the assumption that sexist beliefs are epistemically irrational, each position in the ethics of belief debate lends itself to a different interpretation of the causal claim: purism about epistemic rationality supports a narrow interpretation, while revisionism supports a broad one. After developing each interpretation, I argue that – at the heart of the disagreement between them – is a different story about the normative significance of the fact that evidence about an unfortunate truth has a sexist provenance. Along the way, I consider what it means for evidence to be “stacked in favor” of sexist beliefs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".