Power, Objectification, and Recognition of Sexualized Women and Men
Bibliographic record
Abstract
In contemporary society, sexual objectification is usually thought of as something that men do to women. However, this notion risks conflating the gender of the perpetrator with the fact that men often hold more social power than women. In the current study, we investigated whether power itself was associated with changes in processing of sexualized human targets, independent of the gender of the power holder. In Experiment 1, we primed separate groups of female participants to high-, low-, or neutral-power. We then engaged them in a recognition task involving upright or inverted sexualized images of men and women. Previous research using stimulus inversion manipulations has found that inversion of faces/bodies, but not of objects, disrupts recognition performance, suggesting a reliance on more configural processing in face/body perception compared to object perception. We found that women primed to high-power did not show an inversion effect for sexualized men but did show an inversion effect for sexualized women. In contrast, women primed to low-power showed an inversion effect for sexualized men and women. In Experiment 2, we replicated this finding and found a similar effect of power for male participants perceiving sexualized images of women. We discuss our results with reference to the literatures on objectification and the cognitive processes involved in the perception of sexualized men and women. Our study provides seminal evidence that power, rather than gender per se, may play a central role in sexual objectification. Online slides for instructors who want to use this article for teaching are available to PWQ subscribers on PWQ's website at http://pwq.sagepub.com/supplemental
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".