Latent Sexism in Print Ads Increases Acceptance of Sexual Assault
Bibliographic record
Abstract
In addition to the more obvious forms of sexism in advertising, media critics and scholars raise concerns about various forms of nonobvious, or latent, sexism (e.g., “dismembered” body parts; makeup possibly resembling a bruise; women in potentially dangerous locations; bodies decorated as products). There is, however, no evidence that the public considers these ads sexist or is affected by them. To determine whether ads promote sexism even if the content is not identified as sexist, participants were exposed to ads containing no sexism, overt sexism, or latent sexism (i.e., content considered sexist by media experts, but not identified as sexist by a lay sample) and then read two vignettes describing incidents of sexual assault and sexual coercion. Participants exposed to ads with latent sexism showed greater acceptance of the sexual assault than did those in the no sexism ad condition and in the overt sexism ad condition. Regarding the sexual coercion vignette, latent sexism did not have the same effects; instead, participants exposed to ads with overt sexism were less likely to minimize the seriousness of the incident than participants in the other ad conditions. Therefore, acceptance of sexual assault can be increased by sexist content in ads even if the content is not identified as sexist. In fact, the evidence suggests that the types of latent sexism in this study produce more deleterious effects than sexism that is easily recognized.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".