Is pornography use associated with anti-woman sexual aggression? Re-examining the Confluence Model with third variable considerations
Bibliographic record
Abstract
The Confluence Model of sexual aggression (Malamuth, Addison, & Koss, 2000) states that pornography use, thought to promote sexual coercion of women through presentation of submissive female imagery, works in conjunction with sexual promiscuity (SP) and hostile masculinity (HM), proposed sexual aggression risk factors, to produce anti-woman sexual aggression. An Internet based survey (N=183 adult males) replicated results of previous Confluence Model research, such that men who were high in HM and SP were more likely to report sexual coercion when they frequently, rather than infrequently, used pornography. Exploring new ground, this study also found that HM and SP together were strong predictors of consumption of violent sexual media, in comparison to non-violent sexual media, which suggests that men at high risk of sexual aggression consume different types of sexual material than men at low risk. Further, individual differences in sex drive were found to account for the effects previously attributed to pornography use in statistical tests of the Confluence Model. In the light of third variable considerations, these findings warrant a careful reappraisal of the Confluence Model's assertion that pornography use is a causal determinant of anti-woman sexual aggression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".