The Media’s Sexual Objectification of Women, Rape Myth Acceptance, and Interpersonal Violence
Bibliographic record
Abstract
A new trend has emerged in print advertisements by which women’s bodies are literally morphed into objects. This study begins to explore this phenomenon by examining the effects of viewing these types of advertisements on attitudes toward rape and violence, as well as rape likelihood. In addition, this study examined the degree to which moral disengagement and dehumanization influences rape likelihood through rape myth acceptance. Three hundred eighty participants viewed 1 of 3 conditions—control, sexual objectification of women, or women as objects—and then filled out a number of questionnaires to assess their rape myth acceptance, acceptance of interpersonal violence, rape likelihood, and moral disengagement. Results indicated that although viewing sexually objectified women in advertising did not increase any of the dependent variables for individuals in the experimental conditions compared to the control condition, there was a main effect of sex for several dependent measures, as well as a full mediation of moral disengagement and rape likelihood by rape myth acceptance in male participants. These findings suggest that education aimed at correcting men’s endorsement of rape myths might be a key pathway to decreasing rape likelihood. Despite these conclusions, this study’s primary limitation was that it was conducted with collegiate participants with an unequal gender distribution.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".