Understanding and Defining Regenerative Agriculture Practices in Alberta: From Producer to Policy
Bibliographic record
Abstract
This paper explores the opportunities for Regenerative Agriculture (RA) in Alberta, aiming to inform effective policy design based on stakeholder input. It examines how farmers define RA, the RA practices they currently utilize, the barriers they experience in implementing RA practices and the opportunities for enabling policy design and implementation. In-depth semistructured interviews were conducted with 11 participants with knowledge and experience with RA in Alberta. The findings reveal that defining RA requires a context-specific approach that considers regional conditions and individual farmer needs. Key barriers to the implementation of RA practices include Alberta’s climate, short growing season and a lack of producer knowledge. Insufficient inclusion of diverse perspectives in agricultural policymaking, disincentives for early adopters of RA and the lack of incentives for farmer participation in policy discussions are identified as policy gaps requiring adjustments. The findings highlight the need for tailored policies that accommodate the diverse needs of farmers while promoting the principles of RA. This study provides valuable insights into how farmers perceive government policies related to RA, offering policy recommendations to help develop more effective strategies to overcome barriers and promote the expansion of RA in Alberta.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".