Steps Toward Making Every Vote Count: Electoral System Reform in Canada and its Provinces
Bibliographic record
Abstract
Steps Toward Making Every Vote Count: Electoral System Reform in Canada and its Provinces, Henry Milner, ed., Peterborough: Broadview, 2004, pp. 319. After some decades of a principally academic debate in Canada, electoral reform has become a topic of current political discussion and even, in some cases, concrete action. Henry Milner's Steps Toward Making Every Vote Count is a very useful follow-up to the widely read Making Every Vote Count (Peterborough: Broadview Press, 1999). While the focus remains on trying to make the case that electoral reform is indeed necessary in Canada, the emphasis lies more on assessing the changes that are already under way. With five provinces directly engaged in electoral reform, this book provides a very welcome collection of essays to deepen our understanding of the subject.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".