Psychological and demographic predictors of support for marriage equality: An Australian survey
Bibliographic record
Abstract
In 2017, the Marriage Amendment Act was passed, which made same-sex marriage (SSM) legal in Australia. Research has identified factors that predict support for SSM. However, cultural and political differences between countries where the majority of research has originated makes generalising findings to Australia difficult. The purpose of this study was to investigate demographic, personality, and social psychological factors as predictors of both attitudes toward SSM and response to the Australian Marriage Law Postal Survey, which preceded the amendment. A sample of Australian citizens (n = 259) over 18 completed an anonymous online survey measuring demographics, religiosity, political conservatism, beliefs about marriage and sexuality, and personality characteristics (including empathy, openness to experience, rightwing authoritarianism, social dominance orientation). A series of separate linear (for SSM attitudes) and logistic (for Postal Survey response) regression models were used to investigate predictors of SSM attitudes and Postal Survey response, which were highly correlated. A range of demographic, personality, and social factors significantly predicted SSM attitudes and support for the legalisation of SSM in Australia. Results suggested that fostering characterises such as empathy, openness to experience, and contact with LGBTI people may increase positive attitudes towards LGBTI people and SSM following the legalisation of marriage equality.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".