Can exposure to sexual objectification impact policy attitudes? Evidence from two survey experiments
Bibliographic record
Abstract
Research in social psychology has long argued that exposure to objectifying portrayals of women can lead to increasingly misogynist attitudes and behavior. We argue that such images can also impact on gendered policy attitudes. We suggest that objectifying images prime sexist attitudes and reduce perceptions of women's agency, warmth, and competence. We argue that this may translate into decreased support for reproductive rights and other gender-salient policies. Furthermore, these effects may vary by the gender of those exposed to these images. In two survey experiments with brief exposures to objectifying images, we find mixed support for these predictions. Although we find some negative effects as predicted, we also find positive effects of objectification among women in the sample that are suggestive of a backlash effect. We discuss potential explanations for this heterogeneity. Overall, our results suggest interesting avenues to further explore the effects of objectification on political outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".