Global Climate Change and the Industrial Animal Agriculture Link: The Construction of Risk
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 This paper examines discourses of stakeholders regarding global climate change to assess whether and how they construct industrial animal agriculture as posing a risk. The analysis assesses whether these discourses have shifted since the release of Livestock’s Long Shadow, a report by the United Nation’s Food and Agriculture Organization, which indicated that the industrial animal agriculture sector as a whole contributes more to global climate change than the transportation sector. Using Ulrich Beck’s theorizing of the “risk society,” this paper examines how various animal rights and welfare groups, environmental organizations, meat industry stakeholders, governmental agencies, and newspapers in Canada, the United States, and internationally investigate and construct industrial animal agriculture as a risk, if at all, and how their respective discourses conflict. The findings indicate that while some stakeholders acknowledge industrial animal agriculture’s contribution to global climate change, for the most part the problematization of animal agriculture has not increased since the release of Livestock’s Long Shadow, and the animal agriculture industry has seemingly not lost its power to “rationalize risk.”
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 | 0.001 | 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 | 0.002 |
| 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