Environmental Harm and “the Good Farmer”: Conceptualizing Discourses of Environmental Sustainability in the Beef Industry
Bibliographic record
Abstract
Abstract Maintaining sustainability discourses in the face of evidence to the contrary is a topic of considerable interest in sociology. We approach this topic with a focus on the beef industry in Alberta, Canada. By studying the discourses of cow and calf producers this article addresses the following questions: (1) What are the discourses that producers draw on to support their self‐perceptions as stewards of the land, (2) how are these discourses used by producers to negotiate and reconcile their involvement in a system that contributes to environmental degradation, and (3) what are key elements to interpreting these discourses of sustainability? Methods include semistructured interviews with attention to the potential of genomics for enhanced cattle breeding to ameliorate harmful methane emissions. Our findings indicate that producers draw on narratives of balance between economic and environmental concerns, focus on epistemic nearness, and fragment their understanding of the beef industry to maintain discourses of sustainability. These findings offer insights into the impacts that embodied, material forms of knowledge have on farmers’ perceptions of the land, and demonstrate that these narratives and ways of knowing predicate farmers’ understandings of sustainable development.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.082 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".