Bridging the divide: Understanding the psychological factors influencing feminist women's support to transgender related policies
Bibliographic record
Abstract
Abstract The demand for trans people's institutional rights and the approval of the Trans Law (Law 04/3023) has polarized the feminist movement in Spain. In this contentious context, our studies examined the relationship between feminist identity and support for, or opposition to, trans rights among the University community. Two correlational studies (Study 1a = 317; Study 1b = 323) conducted before the law's passage provided opposing results regarding the association between feminist identity and support for trans rights. Building upon these findings, two experimental studies (Study 2 N = 415; Study 3 N = 405) exposed ciswomen to cooperation or conflict narratives and examined their impact on reactive threat, zero‐sum beliefs, and support for pro‐trans or anti‐trans collective actions. Conflict narratives increased reactive threat and zero‐sum beliefs, leading to more anti‐trans and fewer pro‐trans actions. Additionally, a direct positive link was observed between feminist identification and support for pro‐trans actions, while a negative association was found with anti‐trans actions (opposite for ideological threat). Based on these findings, we propose a series of win‐win strategies to support trans rights and promote peacebuilding and inclusivity in Spanish universities without triggering threat or zero‐sum beliefs in ciswomen.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".