Stakeholder Perceptions of Pluralistic Extension and Advisory System in Ontario
Bibliographic record
Abstract
Ontario's agricultural extension and advisory services (EAS) have transitioned to a pluralistic system, driven by evolving challenges. While new methods and structures exist, stakeholder perceptions of advisory effectiveness remain largely unexplored. This paper discussed synthesis of three interconnected Q-methodology studies to understand stakeholder prioritization of advisory methods, evaluation of pluralistic EAS performance, and assessment of advisory source usefulness. Forty-nine purposively selected stakeholders completed online Q-sorts via Qualtrics. Principal component analysis and qualitative insights revealed distinct perspectives. Study 1 identified three method preferences: Factor 1 (Personalized)—producers emphasized one-on-one, farm-specific consultations for tailored solutions; Factor 2 (Digital)—researchers and tech-oriented advisors valued webinars/apps for scalability; Factor 3 (Traditional)—advisors prioritized field days/printed materials for trust-building. Study 2 on system performance revealed three viewpoints: Perspective I prioritized service quality; Perspective II demanded integrated governance and quality; Perspective III linked governance structures to method selection to resolve redundancy and bias. Study 3 found 42% of stakeholders preferred an integrated approach; others leaned toward formal-only sources or peer-oriented networks, noting concerns over information impartiality in informal channels. Optimizing Ontario’s pluralistic EAS requires: (1) cohesive governance frameworks fostering actor collaboration and transparent accountability; (2) hybrid advisory models balancing in-person and digital delivery; and (3) structured pathways to enhance reliability of informal sources while preserving their trust-based relationships.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".