Bibliographic record
Abstract
Abstract: When Mike Harris's Conservative government came to power in Ontario, it embarked on a program of significant municipal reform. Municipal amalgamation, one of the major elements of this reform package, had many goals, two of which were “efficient service delivery” and provision of “high-quality services at lowest possible cost.” This article reviews three amalgamations, which reduced the number of municipalities from twenty-nine to three, to determine whether the mergers resulted in a change in the quality of service delivery. In all three cases, there was a great outpouring of both positive and negative rhetoric in the lead-up to the amalgamations, but not much happened. There were clearly pockets of dissatisfaction, but most residents did not see a significant change in the quality of services. Sommaire: Lorsque le gouvemement conservateur de Mike Harris est arrivé au pou-voir en Ontario, il s'est lancé dans un programme de réformes municipales importantes. Le regroupement de municipalités, l'un des principaux éléments de cet ensemble de réformes, avait plusieurs objectifs, dont deux d'entre eux étaient une prestation efficiente de services et l'offre de services de grande qualité au prix le plus bas possible.Le présent article passe en revue trois regroupements, qui ont réduit le nombre de municipalités de vingt-neuf à trois, pour déterminer si les fusions ont entraîné un changement dans la qualité de la prestation de services. Dans les trois cas, il y a eu une extraordinaire effusion de rhétorique, à la fois positive et négative au cours de la période précédant les regroupements, mais peu se passa. I1 y a eu de toute évidence des foyers de mécontentement, mais la plupart des résidents n'ont pas remarqué d'importants changements dans la qualité des services.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".