A multilevel analysis of LGBT (Lesbian, Gay, Bisexual, Transgender) rights support across 77 countries: The role of contact and country laws
Bibliographic record
Abstract
Although intergroup contact reduces prejudice generally, there are growing calls to examine contextual factors in conjunction with contact. Such an approach benefits from more sophisticated analytic approaches, such as multilevel modelling, that take both the individual (Level-1) and their environment (Level-2) into account. Using this approach, we go beyond attitudes to assess both individual and contextual predictors of support for gay/lesbian and transgender rights. Using a sample of participants across 77 countries, results revealed that personal gay/lesbian contact (Level-1) and living in a country with more gay/lesbian rights (Level-2) predicted greater support for gay/lesbian rights (n = 71,991). Likewise, transgender contact and living in a country with more transgender rights predicted more support for transgender rights (n = 70,056). Cross-level interactions are also presented and discussed. Overall, findings highlight the importance of both individual and contextual factors in predicting support for LGBT communities.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".