Birds of a feather? The role of partisanship in the 2003 Ontario government transition
Bibliographic record
Abstract
The question posed in this article focuses on whether partisanship affects government transitions. The way governments assume power in Canada is an intriguing part of politics, but we have only a limited knowledge of transition mechanics. There has been no investigation into whether transitions have an inter-governmental dimension, for instance, nor have transitions been studied with the government's partisan complexion in mind. This article addresses this gap in the literature by examining the 2003 Ontario Liberal transition through inter-governmental and political lenses. Using indicators to assess the role and impact of partisanship, the Ontario case suggests that partisan complexions matter in how the transition process is planned and executed. The provincial team will seek advice on the matter from their federal cousins, for example, and will recruit experienced political staffers from Parliament Hill to be part of the new sub-national government. Shared partisanship may also predict whether harmonious relations with Ottawa will prevail during the first several months in power. The article concludes by asserting that in provinces where political party organizations formally overlap or are informally intertwined across the federal divide, partisanship can play a role in determining how a new government prepares and implements its transition process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".