Super‐users and hyper‐experts in the provision of policy advice: Evidence from a survey of Canadian academics
Bibliographic record
Abstract
Abstract The relationships of influence and activity between academics and other actors (public, private, and non‐governmental) in the policy process are complex. Although older work often argued academic research at best had an indirect “environmental” or “enlightenment” effect on policy‐makers, (May et al. (2016). Journal of Public Policy, 36, 195) recently argued that in the US case previous studies misconstrued the role of academic policy advice because they surveyed “average” academics and in so doing missed the significant impact of a small elite group of “hyper‐experts” within an already small group of “super‐users” interacting on a constant basis with government policy‐makers. This article draws upon data from a survey of academics in four fields (Business, Engineering, Health and Politics) in six major Canadian Universities to map out the relationships existing between academics and other actors in the public, private, and non‐governmental sectors and test for the existence of this elite pattern of interaction in a second country.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".