Bibliographic record
Abstract
Jean Charest and Dalton McGuinty were both elected as premiers of their respective provinces of Quebec and Ontario in 2003; they had both opposed municipal mergers while they were in opposition; and they both had promised while in opposition to create a mechanism for municipal demergers. Charest followed through on his promise and demergers have taken place; McGuinty reneged. Most observers would probably judge that McGuinty has handled the issue more effectively than Charest. The purpose of this paper is to describe the similarities and differences in the political context in which both leaders were working, to explain their different responses, and to assess the relative merits of the different approaches taken by the two premiers.. Events in the two provinces will be described chronologically. This is because there is ample evidence that political actors in each province were paying close attention to, and were affected by, what was going on in the other. There is little or no documentary evidence to support the assertion that Ontario actors were affected by events in Quebec, but anyone who conversed with relevant government officials and demerger activists in Ontario would be aware of their interest in recent events in what was going on in their neighbouring province.1 Despite its title, this paper does not focus on policy-making in the two provinces relating
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".