Participatory Systems Mapping Network Analysis: Drivers and Barriers Influencing Use and Uptake of BMP in the Potato Production System-South Ontario
Bibliographic record
Abstract
Several factors (drivers and challenges) influence on farmers’ to adopt best management practices (BMP) in the potato sector. These factors can be structural, institutional, and organizational. They may also be related to diverse components in the potato sector system. In this presentation, we share the results of a series of workshops conducted with potato farmers (small, medium, and large scale) to collaboratively develop a network causal map of the factors influencing the adoption of Best Management Practices in the potato sectors. The map was intended to represent what potato farmers perceived and believed to be the causal structure that influences the adoption of BMPs. The following objectives guided the workshops:1. Confirm what sustainable practices are currently being used within the sector. 2. Identify factors that influence farmers’ motivation and uptake of these practices. 3. To identify current conditions that either promote or hinder the uptake, adoption and sharing of these practices. 4. To identify possible strategies for strengthening support, uptake and sharing of these practices to increase their dissemination and use. If we do not have time, this can be done in a desk analysis using the network analysis. The results are going to contribute to policy making.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".