Policy analysis and policy work in federal systems: Policy advice and its contribution to evidence-based policy-making in multi-level governance systems
Bibliographic record
Abstract
Abstract In most cases, policy scholars interested in the role of policy analysts in promoting and practicing evidence-based policy-making rely on very partial survey results, or on anecdotal case studies and interview research. Despite the existence of a large body of literature on policy analysis, large-scale empirical studies of the work of policy analysts are rare, and in the case of analysts working at the sub-national level, virtually non-existent. There has been very little research on this level of policy workers despite the significant powers they exercise in prominent federal systems such as the USA, Germany, Australia, Mexico, Russia, Brazil, Malaysia and Canada. This paper reports on the first comprehensive survey of the work of policy analysts at the provincial and territorial levels conducted in Canada in 2008–2009. It examines the background and training of provincial and territorial policy analysts, the types of techniques they employ in their jobs, and what they do in their work on a day-by-day basis. The resulting profile of sub-national policy analysts presented here reveals several substantial differences between analysts working for national governments and their sub-national counterparts, with important implications for policy training and practice, and for the ability of nations to improve their policy advice systems in order to better accomplish their long-term policy goals through the practice of evidence-based policy-making.
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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 | 0.100 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".