How is Climate Change Changing Agrarian Studies?
Bibliographic record
Abstract
ABSTRACT A range of compelling recent literature highlights how climate change is rewriting the intertwined social and environmental processes that comprise agrarian landscapes. Mainstream reaction has been to double down on technical intensification strategies supplemented by a resolute faith in scientific advancement to reduce vulnerabilities. For critical agrarian studies, however, climate change raises new conceptual and methodological challenges. Has climate change reinforced or undermined existing concepts and frameworks that explain core dynamics of agrarian change? Does agrarian studies as a field of engaged research need to change alongside the climate? In this exchange our contributors consider how anthropocentric climate change requires the field to rethink core analytical categories within agrarian studies. Key questions that the forum addresses include: How does climate change validate and/or challenge the conceptual armoury and normative orientations inherited largely from Marxist‐influenced political economy? What new concepts and theoretical influences will prove helpful in orientating agrarian studies within a changing climate? How do we synthesise these with existing frameworks and concerns? And how does this reformulation change our understanding of the forms and content of resistance within agrarian environments?
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".