From Adoption to Adaptive Learning: The Need for Farmer-Centric Soil Health Extension and Education Evaluation
Bibliographic record
Abstract
In the Canadian agri-food system, there is a growing and perceived need to evaluate soil health from the perspective of farmers. Ultimately, farmers determine if and how they adopt inputs including technologies and expert advice. Adoption is a term often used to refer to a farmer’s decision to change a management practice. This is however, a limited understanding of the term, which may not appreciate the wholeness of understanding and adapting a management practice on-farm. This paper uses a meta-analysis methodology to explore the need for farmer-centric soil health extension and education assessment. The studies reviewed suggest that the social elements of soil health may be more relevant to understanding sustainability transitions than a focus on the adoption of specific management practices alone. An understanding that gives weight to the social influences that guide a farmer’s adaptive management decisions, including practices of leading or learning-by- doing, self-awareness, and the role of social interactions within those practices and interventions. Exploring the ‚social side of soils, comes at a time when the Ontario strategy for soil health entitled ‚New Horizons‚ has set out a framework in which to support farmer-centric soil health extension and education. Funding: NSERC CREATE Climate Smart Soils
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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 | 0.501 | 0.564 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".