Assessing policy capacity in Canada's public services: Perspectives of deputy and assistant deputy ministers
Bibliographic record
Abstract
Abstract: The state of policy capacity within Canada's various levels of government has for some time been the subject of discussion both within the public services themselves and among the academic research community. Drawing on the results of a 2006 survey of deputy and assistant deputy ministers working in Canada's federal, ten provincial and three territorial governments, this article presents assessments made by the most senior leadership. The survey results show that ninety per cent of deputy ministers and assistant deputy ministers agree that policy capacity has changed but that the change is not uni-directional. Both improvements and decline in policy capacity were observed, although assessments of decline were somewhat more pronounced. Moreover, improvements in policy capacity were found to be associated with a reduced focus on direct service delivery, a greater concern with long-term planning, and the presence of a political leadership interested in innovation. Conversely, declining policy capacity was found to be linked to centralization of power, the loss of institutional memory, and “churning” within the ranks of the executive leadership. Additionally, level of government was also observed to be linked with change in policy capacity, with provincial deputies reflecting more negatively on policy capacity decline in their government than deputies at other levels.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".