Super‐users and hyper‐experts in the provision of policy advice: Evidence from a survey of Canadian academics
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
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.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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