Farmer identities: facilitating stability and change in agricultural system transitions
Bibliographic record
Abstract
The need for institutional change posed by anthropogenic global warming is now well-recognized, and this is particularly the case for agri-food systems, which are both significant contributors to climate change, and highly vulnerable to its impacts. The importance of identity to institutional change is well-recognized in various areas of scholarship, although in the study of institutional responses to climate change this key driver is less often discussed. In this study, we seek to create space for doing so, by focusing on the identity work of a sample of farmers in Alberta, Canada, as they navigate this moment of sector uncertainty. We show how farmer identities are becoming destabilized as producers attempt to accommodate growing environmental and climatological concerns, with many productivist farmers seeking to deflect sources of identity disconfirmation, while post-productivist farmers engage in active community-building and information seeking to support the formation of a new identity.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".