POLICY ADVICE IN MULTI-LEVEL GOVERNANCE SYSTEMS
Bibliographic record
Abstract
Despite the existence of a large body of literature on policy analysis, empirical studies of the work of policy analysts are rare, and in the case of analysts working at the sub-national level in multi-level governance systems, virtually non-existent. Many observers decry the lack of even such basic data as how many policy analysts work in sub-national government, on what subjects, and with what effect. This is true in many countries, for example, the U.S., Germany, and Canada, all federal systems with extensive sub-national governments but where what little empirical work exists focuses on government at the national level. In most cases, in justifying their observations and conclusions observers rely on only one or two quite dated works, on very partial survey results, or on anecdotal case studies and interview research. This article reports the findings of a 2008-2009 survey aimed specifically at examining the background and training of provincial policy analysts in Canada, 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 training and for the ability of nations to accomplish their long-term policy goals.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".