Public policy and operational alignment in light of public service retrenchment – lessons learned from Canada
Bibliographic record
Abstract
This article examines the Strategic and Operating Review (SOR) process used by the Government of Canada through a strategic management perspective. Initiated by the Harper government in the 2011 Budget as a one-year process, SOR is expected to secure savings of CDN$4 billion by 2014–15 from the CDN$80 billion operating budget of departments. Our article assesses to what degree the strategic operational cuts support the public policy priorities of the Harper government. Points for practitioners Using Canada as a case study to understand how budgetary cuts are handled, this article provides an opportunity to consider how policy makers align operational cuts with public policy priorities. While the budget cuts in this case study are operational in nature, they require direction from central government to support – not undermine – public policy priorities.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".