The politics of red meat consumption and climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it