From learner to leader: exploring learning, motivations, and roles of regenerative grazing mentors
Bibliographic record
Abstract
Increasing interest in sustainable beneficial management practices (BMPs) such as those advocated by regenerative agriculture have led to a proliferation of programs encouraging adoption. Mentors including teachers, consultants, and peer farmers can significantly influence adoption decisions. While research in climate-smart agriculture has highlighted mentor support in a mentee’s learning pathway, little research has turned the learning lens to the mentors and examined the pathways through which they arrived as leaders and mentors in their fields and their learning process. This study examined the Farm Resilience Mentorship Program (FaRM) in Canada. In 2022, FaRM recruited 26 mentors across Canada and provided training in mentorship and advanced grazing systems. Through interviews, we explored 12 program mentors’ adoption process and the role support networks and mentors had in their learning pathways. Our findings highlight how diverse social connections support motivations to learn and adoption and how the adoption experience and support systems inform mentor approaches to teaching and knowledge exchange. These findings have implications for how programs can support the development of mentors and enhance engagement in training programs improving sustainable production and farm resilience.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".