Trends and directions in Canadian policy analysis and policy advice
Bibliographic record
Abstract
As public sector work, policy analysis and policy advising is the soft craft of hard choices. Changes in the context and content of Canadian politics and government in recent decades have shifted the nature of public service policy advice giving. This chapter presents these changes and their implications in relation to two approaches to policy advising. One model, the traditional approach in Canadian parliamentary governments, involves public servants speaking truth to those in power, namely cabinet ministers. The second model, reflecting contemporary trends in governance, can be described as many actors sharing many truths to decision makers. In short, there has been a shift in policy advisory systems engaged in policy analysis and matters of giving policy advice. The chapter examines each of these models, describing them and offering some criticisms, as well as noting trends in Canada that relate to this altered context of advising and policy development.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.046 |
| Science and technology studies | 0.029 | 0.032 |
| Scholarly communication | 0.039 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".