The politics of red meat consumption and climate change
Bibliographic record
Abstract
Abstract Red meat production is one of the leading sources of carbon dioxide emission thus reducing meat production and consumption is crucial. Using a sample of American adults (n = 456), the link between right-wing sociopolitical ideologies and (i) attitudes towards red meat; (ii) willingness to reduce red meat consumption; (iii) willingness to pay more for red meat; (iv) belief about the impact of red meat consumption on the environment; and (v) and distrust (versus trust) of authorities was examined. Right-wing ideologies (i.e. right-wing-authoritarianism and social dominance orientation) were associated with more positive attitudes towards red meat, unwillingness to consume less red meat or pay more for red meat, disbelief that red meat negatively impacts the environment, and greater distrust of information from authorities that propose a link between red meat production and negative environmental impact. However, results varied by political ideology dimension. Findings suggest that attempts to alter peoples’ red meat consumption—as part of a strategy for tackling climate change—must incorporate a nuanced understanding of the impact of sociopolitical ideologies on attitudes towards red meat consumption and the need to raise awareness about its impact on the environment.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".