If given the choice, would you choose to be a man or a woman?
Bibliographic record
Abstract
Recent research in North America has failed to find evidence of sexist attitudes on measures of explicit sexual attitudes. This suggests that social desirability may affect self-reports of gender-related attitudes. This study used an indirect means to assess gender-related attitudes—individual's interest in experiencing being the other gender. Participants were 209 individuals (107 men, 102 women) who completed an online survey. Participants indicated whether they would choose to be reincarnated as a man or a woman and whether they would choose to experience being the other gender on a temporary basis. They also provided the reason for their choices. We found that 30% indicated that they would choose to be the other gender if reincarnated, 56% for a week, 67% for a day, and 65% for an hour. There were no significant gender differences. Content analysis of responses indicated three primary reasons for choosing to experience being the other gender: wanting a new experience or perspective; the perceived positives of being the other gender; and, the perceived negatives of being their current gender. It also yielded three primary reasons for choosing not to experience being the other gender: desire to maintain the status quo; the perceived positives of their current gender; and, the perceived negatives of the other gender. Many participants also identified the temporary nature of the change as important to their decisions regarding a time-limited experience of being the other gender. The results are discussed in terms of the insights they provide on implicit gender-related attitudes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".