Experimental effects of degrading versus erotic pornography exposure in men on reactions toward women (objectification, sexism, discrimination)
Bibliographic record
Abstract
There is considerable debate about the potential harmful impacts of pornography exposure and viewing among men. The current literature suggests that heterosexual men’s use of pornography may be associated with negative attitudes and behaviour toward women. However, little research has experimentally examined exposure to different types of nonviolent pornography, using a range of outcome variables, and differentiating effects for women generally versus the porn actress. In the current study, 82 undergraduate men were randomly assigned to one of three conditions (degrading, erotica, or control); within each condition they were randomly assigned to watch one of two approximately 10-minute clips: degrading pornography (i.e., nonviolent, debasing, dehumanizing), erotic pornography (i.e., non-degrading, nonviolent, consensual), or a news clip as a control condition. After watching the clip, measures of subjective sexual arousal, objectification of the specific woman in the clip, essentialism of women, ambivalent sexism, and discrimination against a fictitious woman were completed. Exposure to erotica ( vs. degrading) generated less objectification of the porn actress; exposure to erotica ( vs. control) also generated the greatest discrimination toward the fictitious woman, although the omnibus for the latter was non-significant. Exposure to degrading pornography ( vs. erotica or control) generated the strongest hostile sexist beliefs and the greatest amount of objectification of the woman in the clip. Thus, pornography use may not be generally harmful or harmless, but the effect of pornography exposure may depend on the type of pornography and the specific outcome. Implications for debates about the potential negative impact of pornography exposure 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".