Internal governmental performance and accountability in Canada: Insights and lessons for post‐pandemic improvement
Bibliographic record
Abstract
Abstract This article summarizes and expands on the work of one of three dialogue study teams organized by the Canada School of Public Service (CSPS), Institute of Public Administration of Canada (IPAC), and the Canadian Association of Programs in Public Administration (CAPPA). The Internal Accountability Dialogue Study Team explored the rationale and effects of the performance management regime of the Canadian federal government starting the fall of 2021. In particular, the study team wanted to know whether and in what ways the performance management regime provided information and other support to decision‐makers during the pandemic, and in what ways the efficacy of the function post‐pandemic could be improved. It found that despite promises to bolster accountability and decision‐making, and to foster a learning culture within government, it was subject to a strong control orientation reminiscent of traditional public administration (TPA) that obstructed its potential to contribute in effective ways. If anything, practitioners indicated that performance reporting was something to be avoided rather than embraced raising concerns about the usefulness of the function. The article concludes that performance management ought to be revisited with a clear orientation to learning rather than control, which may restore confidence in its relevance for decision‐making.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.026 | 0.021 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".