Bibliographic record
Abstract
Declining voter participation, strategic-voting campaigns, public opinion polls, and myriad other signals highlight the need to improve Canada's current first-past-the-post electoral system. How the system ought to be changed, however, remains unclear. This paper briefly reviews candidate models for electoral reform from other nations, before putting forward the parsimonious mixed-member (PMM) model. This model was inspired by the mixed-member (MM) proportional representation system, as currently used in, for example, Germany. It is 'parsimonious', however, in the sense that the number of additional MPs brought into parliament to reach proportionality is minimized. Like traditional MM, PMM preserves the individual relationship between every voter and an MP, and it eliminates under-representation of parties. Unlike traditional MM, however, it also preserves the incentive for parties to win local races, it avoids unnecessary dilution of constituency representatives, and it avoids misattributing spoiled ballots to major parties. As such, it can be thought of as an optimization of MM. A key feature of this model is conservatism -it modifies our existing system with the minimal set of changes necessary to resolve the most obvious problems in the current system, without creating new ones. It fixes only what is broken.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".