Academics in the Policy Advisory System
Bibliographic record
Abstract
This chapter examines the evolving role of academics within the policy advisory system (PAS), with a specific focus on Canadian academia. Despite academia&s;s potential to enhance public policy-making through knowledge creation, its impact remains limited due to structural, linguistic, and temporal misalignments with governmental priorities. Drawing on a survey of 505 Canadian academics across disciplines such as health studies, engineering, political science, and business, this research evaluates the extent and nature of academic engagement in policy advisory roles. Findings reveal that academics seldom provide policy advice, with health and political science experts being the most engaged in public sector research. Informal interactions dominate, though formal involvement as advisory committee members occurs infrequently. The study also highlights the role of ‘hyper-experts’, a small yet influential group, who act as knowledge brokers within the PAS. These findings underscore the need for strengthened academic-practitioner collaborations and institutional support to enhance academics’ contributions to evidence-based policy-making. The chapter concludes with recommendations to bridge the academic-practitioner divide and foster co-production of policy knowledge in Canada&s;s complex and evolving PAS landscape.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".