Farmer Knowledge and Risk Analysis: Postrelease Evaluation of Herbicide‐Tolerant Canola in Western Canada
Bibliographic record
Abstract
The global controversy regarding the use of genetically modified (GM) crops has proved to be a challenge for "science-based" risk assessments. Although risk analysis incorporates societal perspectives in decision making over these crops, it is largely predicated on contrasts between "expert" and "lay" perspectives. The overall objective of this study is to explore the role for farmers' knowledge, and their decade-long experience with herbicide-tolerant (HT) canola, in the risk analysis of GM crops. From 2002 to 2003, data were collected using interviews (n= 15) and mail surveys (n= 370) with farmers from Manitoba and across Canada. The main benefits associated with HT canola were management oriented and included easier weed control, herbicide rotation, and better weed control, whereas the main risks were more diverse and included market harm, technology use agreements (TUAs), and increased seed costs. Benefits and risks were inversely related, and the salient factor influencing risk was farmer experiences with HT canola volunteers, followed by small farm size and duration using HT canola. These HT volunteers were reported by 38% of farmers, from both internal (e.g., seedbank, farm machinery, etc.) and external (e.g., wind, seed contamination, etc.) sources, and were found to persist over time. Farmer knowledge is a reliable and rich source of information regarding the efficacy of HT crops, demonstrating that individual experiences are important to risk perception. The socioeconomic nature of most risks combined with the continuing "farm income crisis" in North America demonstrates the need for a more holistic and inclusive approach to risk assessment associated with HT crops and, indeed, with all new agricultural technology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".