Producer perceptions of, and barriers to, implementations of insect resistance management (IRM) strategies for genetically engineered BT-containing corn in Ontario
Bibliographic record
Abstract
Genetically-engineered, Bt-containing corn was the first product of modern crop biotechnology available to Ontario corn producers that would aid in protecting field corn insect damage caused by the European corn borer (ECB). With this new technology corn producers are now able to control ECB effectively and cost efficiently. The goal of this research was to identify farmers' initial attitudes and perceptions towards Bt-corn that might facilitate or impede the adoption of both Bt-corn and Insect Resistance Management (IRM) strategies across Ontario. By developing a baseline understanding of the influencing factors that surround Bt-corn, recommendations for an effective communication strategy were developed. The methodology included interviewing 15 agricultural leaders in Ontario from diverse backgrounds to help develop a self-administered questionnaire, in addition to an extensive review of scientific literature and in-depth analysis of media coverage. In early April 1998, 2, 435 questionnaires were sent to a random list of corn producers across Ontario. Results are based on 780 returned surveys. A follow up survey of over 800 corn producers was conducted in June 1999 to identify emerging trends. Bt-corn was used extensively by Ontario corn producers during the 1998 and 1999 growing-season. The issue is not whether producers will continue to use the technology, but will growers practise vital resistance management whenever they grow Bt-corn. There is a need for continued support of harmonized recommendations on IRM so that farmers are clear about what they need to do to delay ECB resistance to the Bt protein. Furthermore, support by key stakeholders can also add to the credibility of the agri-food industry in communicating the benefits and risks associated with crop biotechnology. By demonstrating sound stewardship and communicating how and why the technology is used, the agri-food industry can improve consumer confidence in plant biotechnology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".