A Comparative Assessment of Elite Policy Recruits in Canada
Bibliographic record
Abstract
Recent case studies and large-N survey evidence has confirmed long-suspected shortages of public sector “policy capacity”. Studies have found that government policy workers in various jurisdictions differ considerably with respect to types of policy work they undertake, and have identified uneven capacity for policy workers to access and apply technical and scientific knowledge to public issues. This suggests considerable difficulties for government’s ability to meet contemporary policy and governance challenges. Despite growing attention to these matters, studies have not examined the “elite” policy workers many governments recruit to address these capacity shortages. Using an established survey instrument, this study of two Canadian recruitment programs provides the first comparative analysis of elite policy recruits, as policy workers. Three research questions anchor the study: (1) What is the profile of these actors? (2) What types of policy work do “elite” policy analysts actually engage in? (3) How does their policy work compare by recruitment program? The article provides fresh comparative data on the nature of elite policy work and policy analytical capacity, but, more importantly, a crucial baseline for future comparative study of how elite recruitment may facilitate “supply-side” capacity gains expected from recruitment programs.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".