Sunshine, Scrutiny, and Spending Review in Canada, Trudeau to Trudeau: From Program Evaluation and Policy to Commitment and Results
Bibliographic record
Abstract
Abstract: This review surveys experience with evaluation practices in the government of Canada since the mid-1960s, particularly with respect to spending reviews, concluding that there is little reason to expect any direct link from ongoing evaluation practices to cabinet decisions. The renewed commitment to evidence-based decision-making announced by the new Liberal government is unlikely to change this conclusion. The introduction of deliverology as a support function centred in the Privy Council Office shifts attention from policy formation to implementation and program delivery, with important emphasis on innovation and adaptation. But the crucial challenge still rests in achieving greater public access to information and greater inclusiveness in decision processes. For academic leaders in public administration, attention now should shift from terminological and doctrinal disputes to anticipating the important consequences of machine learning and artificial intelligence for education and future professional practice in public policy.
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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 | 0.059 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".