Steps toward making every vote count: electoral system reform in Canada and its provinces
Bibliographic record
Abstract
List of Tables and Figures Notes on Contributors Preface Introduction: Political Drop-Outs and Electoral System Reform Henry Milner Part I: The Pros and Cons of Reforming the Canadian Electoral System 1. Regionalism and Party Systems: Evaluating Proposals to Reform Canada's Electoral System Harold J. Jansen and Alan Siaroff 2. That Bleak? Fathoming the Consequences of Proportional Representation in Canada Louis Massicotte 3. Problems in Electoral Reform: Why the Decision to Change Electoral Systems is Not Simple Richard S. Katz 4. Reminders and Expectations about Electoral Reform John C. Courtney Part II: Recent Experience in Other Countries 5. Stormy Passage to a Safe Harbour? Proportional Representation in New Zealand Jack H. Nagel 6. Making Every Vote Count in Scotland: Devolution and Electoral Reform
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".