Consuming Pornography Predicts Sexual Harm Against Women, but Only When Consumers Are Higher in Hostile or Benevolent Sexism: Multimethod Research Evidence
Bibliographic record
Abstract
Whether pornography use predicts sexual harm toward women has been debated, with past research producing contradictory findings. To clarify mixed results and qualify existing theoretical frameworks, I examined the viability of a prejudice-based person-by-situation approach focusing on hostile and benevolent sexism as essential moderators of potential anti-women effects of pornography, namely rape myth acceptance, sexual objectification, and sexual harassment. Study 1 revealed that for men (n = 379) and women (n = 278) higher but not lower in hostile or benevolent sexism, more frequent pornography consumption related to higher anti-women outcomes. Interestingly, however, women lower in hostile sexism exhibited lower anti-women outcomes with more frequent pornography consumption. In Study 2 (N = 253), I explored the dynamic processes of hardcore pornography use and anti-women effects over time. Notably, those high but not low in hostile sexism sexually objectified women more often when they watched more hardcore pornography in previous weeks, but not vice versa. Study 3 explored the effects of brief pornography exposure and the role of sexual arousal. Among men (n = 500) and women (n = 298), exposure to pornography (vs. control) images increased sexual arousal. Although exposure to hardcore (vs. romantic) images generally lowered sexual arousal, those higher in hostile or benevolent sexism were more aroused by the hardcore (vs. romantic) pornography images. Increased sexual arousal correlated with a) higher sexual objectification (regardless of hostile or benevolent sexism) and b) higher rape myth acceptance or sexual harassment inclinations, but only for those higher in hostile or benevolent sexism. For women lower in hostile sexism, increased sexual arousal correlated with lower rape myth acceptance. With Studies 1-3 demonstrating that anti-women effects are absent or reverse among those lower in ideological sexism, Study 4 delved into ethical pornography consumption in a sample of self-identified feminist women (N = 198). Qualitative data analyses revealed the importance of consent, pleasure, sexual freedom, and the distinction between fantasy and reality as participants navigated ethical tensions. Women overall sought pornography that reflected what they enjoyed in their sex lives, resolving tensions with strategies prioritizing their sexual tastes. Theoretical and practical implications are discussed.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".