Acceptance of female public toplessness: Structural, contextual, and individual predictors of support
Bibliographic record
Abstract
Despite frequent attention in contemporary culture, little empirical research has been done on acceptance of female public toplessness during the past 20 years. As a replication and extension of prior work (Fischtein et al., 2005), an international survey of 314 women and 86 men reported their opinions toward the legality of female toplessness in three public settings. Overall, support ranged from 58% to 76% and was higher than observed in prior surveys from the 1990s. Context, or the specific location where toplessness occurs, remained an important consideration for support. In addition, demographic and attitudinal factors served to predict support and opposition to public toplessness. Specifically, higher religiosity and lower attitudes toward sexual permissiveness were associated with opposition to public toplessness, whereas, lower child protectiveness beliefs and lower trait disgust sensitivity were associated with support of public toplessness. Although men were generally more supportive of public toplessness, gender did not emerge as a significant predictor. Sampling differences and concerted efforts through social media and political campaigns that occurred between prior surveys and the current study may explain, at least in part, the increased overall support and lack of gender differences. As such, these findings may have important societal and legal implications for the acceptance of female public toplessness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".