Proportional-first-past-the-post: \na Canadian model of proportional representation
Bibliographic record
Abstract
For more than a decade a majority of Canadians have consistently supported the idea of \nproportional representation when asked, yet all attempts at electoral reform thus far have failed. \nEven though a majority of Canadians support proportional representation, a majority also report \nthey are satisfied with the current electoral system (even indicating support for both in the same \nsurvey). The author seeks to reconcile these potentially conflicting desires by designing a \nuniquely Canadian electoral system that keeps the positive and familiar features of first-past-thepost \nwhile creating a proportional election result. \nThe author touches on the theory of representative democracy and its relationship with \nproportional representation before delving into the mechanics of electoral systems. He surveys \nsome of the major electoral system proposals and options for Canada before finally presenting \nhis made-in-Canada solution that he believes stands a better chance at gaining approval from \nCanadians than past proposals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".