The futures for regenerative agriculture: insights from the organic movement and the tussle with industrial agriculture
Bibliographic record
Abstract
Concern has been raised about the potential greenwashing/co-optation of regenerative agriculture (RA) due to a lack of consensus on its definition. While the academic literature has cataloged various approaches to defining RA, each definitional approach carries with it a relative concern for its likelihood for co-optation and the potential transformative power it can have within the sector. As the industrial agrifoods sector is taking interest in the field, lessons from the organic movement are worth highlighting. The corporate system has easily integrated the foundational pillar of growing food without chemicals, but left behind the pillars of alternative food distribution, and a focus on whole foods and unprocessed ingredients. Corporate interest in RA could be a major driver for scaled adaptation, yet it may lose its focus on the regeneration of agriculture resources, ecosystems functions, and the social systems required to reproduce the next generation of farmers. The greatest challenge is that the fundamental concern is a philosophical one, which entails a shift in how humans perceive the natural world and their role in it. As RA scales, will it hold to its values and remain obscure, or could its values merge with the predominant industrial system to have significance and affect real change in agriculture?
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".