Stakeholder Perceptions of Pluralistic Extension and Advisory System in Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it