Words not deeds: the weak culture of evidence in the Canadian policy style
Bibliographic record
Abstract
Abstract The Canadian policy style has been described as one of overpromising and underdelivering, where heightened expectations are often met by underwhelming outcomes. Here, we examine the evidentiary style of Canadian policy-making which undergirds and reflects this policy style, particularly the nature of the policy advisory system that contributes to this pattern of policy-making. We do so by assessing how the different components of the advice system, which include academics, consultants, and policy professionals within the public service, are structured and relate to each other within the overall dynamics of information management and policy formulation in the governments of Canada. Using examples from recent efforts to revitalize Canadian government, the paper argues that the federal government in particular shows a pattern of the predominance of non-innovative academic “super-users,” distributed policy shops, and process-oriented analysts and consultants who combine with attributes of federalism and partisan budgetary politics to drive a distinctively fragmented and procedurally-oriented federal policy-making process. In these processes, evidence is often secondary to political posturing and short-term electioneering in program creation and execution, contributing greatly to the national policy style set out above.
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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 | 0.048 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.042 | 0.066 |
| Scholarly communication | 0.029 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| 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".