The Long Run Performance of Decentralized Agencies in Québec: A Multi-dimensional Assessment
Bibliographic record
Abstract
New public management (NPM) was introduced in Western economies many years ago. In Canada, reforms inspired by NPM were introduced in the late 1980s both at the federal and provincial levels. In the specific case of the province of Québec, reforms can be traced back to the mid nineties when the government moved to decentralize operational authority in exchange for more accountability in some government organizations. There is little empirical evidence on the effects of Québec’s aforementioned reforms or on the influence of similar reforms around the world on performance (Boyne, 2003). This study examines the performance of the decentralized Québec agencies over time on multiple dimensions. The preliminary results reported in this document reveal a mixed picture. Standardized performance reported by a majority of agencies in the post LAP period for output and financial indicators show some degree of improvement and we observe similar results for the annual average across agencies for the same categories of indicators. However, there is high variation as some agencies reveal large increases or decreases in average growth for both the pre and post LAP period. Aggregated results for quality do not reveal any significant patterns in one direction or another. Further planned analysis will include examination of quality metrics and uncontrollable factors, as well as detailed examination of agency performance across different dimensions in relation to agency characteristics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".