Delivering Results for Canadians: Improving the Contributions of Enabling Functions
Bibliographic record
Abstract
Abstract Implementing a results‐oriented management system has been a long‐term goal and a challenge for federal public sector organizations in Canada. This article highlights the efforts by the Government of Canada to improve performance measurement, evaluation, and audit (PMEA) functions over time. The article traces administrative reforms since 2000, highlighting the shift from inputs and outputs towards a results‐focused environment aimed at achieving outcomes. However, even after many reforms and goodwill, there remain gaps among the senior policymakers regarding the meaning of “results for Canadians.” The article points out that siloed approaches by function have led to isolated practices and inefficiencies in data sharing and reporting for effectively supporting decision‐making. Although individual functions have improved, implementing a comprehensive results‐focused management architecture continues to pose a significant challenge. The article proposes steps to enhance the effectiveness of PMEA functions, emphasizing the need to integrate functions to enhance public services, decision‐making, and institutional learning.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".