Fleshing Out the Ways Masculinity Threat and Traditional Masculinity Ideology Relate to Meat-Eating and Environmental Attitudes in Australian Men
Bibliographic record
Abstract
Abstract Meat consumption needs to be reduced to limit climate change but achieving this requires understanding the drivers of meat consumption. In this study, we investigated two potential drivers—a contextual threat to masculinity and the stable individual difference of masculine ideology—and how they predict meat-eating intentions, attitudes, and environmentalism. Employing a sample of 375 Australian men, a population known for its high meat consumption, we did not find support that a contextual threat to men’s masculinity increased pro-meat attitudes or intentions. Instead, we found that prevailing views about masculine ideology significantly predicted meat-related attitudes and intentions, with avoidance of femininity associated with lower avoidance of meat and lower intentions to eat clean meat, and the endorsement of male dominance tied to lower pro-environmental responding. Our findings suggest that situational threats to masculinity may not robustly affect meat consumption intentions and highlight the importance of more stable individual differences in the conception of the male gender identity in maintaining men’s high meat consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".