Spending reviews and the Government of Canada: From episodic to institutionalized capabilities and repertoires
Bibliographic record
Abstract
Abstract Following surges of spending and staff hiring to address the COVID‐19 pandemic, the Trudeau government announced a strategic policy review in the 2022 Budget to secure savings of $6 billion. There has been little apparent progress by May 2023 and opaque communications. This is surprising because Canada was once considered an international exemplar for spending reviews, needs to learn from the pandemic experience, has a worrisome medium‐to‐long‐term federal spending trajectory, and the governance and economic context has rapidly evolved. This article identifies different kinds of spending reviews and design considerations, reviews Canada's experience with reviews since the early 1980s, considers recent OECD experience and exemplars, and argues that its spending reviews have become increasingly selective and closed. We suggest the Canadian government should institutionalize annual spending reviews, which can be scaled up or down, and that this points to more fundamental issues for reform and building a new governance culture.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".