Embracing pluralism: assessing the perceptions of different stakeholders on the effectiveness of advisory methods in Ontario
Bibliographic record
Abstract
Purpose Extension and advisory services (EAS) are crucial for farm success and rural well-being in Canada. Like elsewhere, Ontario’s EAS has transformed, embracing diverse providers and methods. While research on pluralistic EAS grows, little examines how stakeholders perceive the effectiveness of different methods. This study fills the gap, evaluating Ontario’s pluralistic EAS methods through key stakeholder perspectives.Methodology Using the Q-methodology, we conducted an online survey on Qualtrics with 49 purposively selected producers, advisors, and researchers and used PQMethod software to analyze their viewpoints.Findings The findings revealed three factors representing the respondents’ views about the usefulness of different advisory methods. The first factor focuses on personalized methods, while the second highlights digitally engaged methods. The third factor stresses traditional extension methods that include group and training-based activities. Researchers’ perspectives leaned towards digital methods, while producers emphasized personalized extension methods, and most advisors were loaded under traditional advisory methods.Practical implications Stakeholders value traditional and personalized extension methods for their interactivity and on-farm presence. The study suggests a personalized advice strategy integrating online, face-to-face, and group methods.Policy implications The findings provide insight for policymakers and practitioners to improve the effectiveness of pluralistic agricultural advisory service delivery by integrating various advisory methods.Theoretical implication This study enriches the discourse on pluralistic advisory systems by presenting stakeholder perspectives on method efficacy.Originality/value This unique study unveils multi-stakeholder views on advisory methods, aiding Ontario farmers to receive relevant, accurate information and meet their needs.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".