Establishing the Rules of the Game: Election Laws in Democracies
Bibliographic record
Abstract
There are an astonishing variety of election laws across contemporary democratic societies. In Establishing the Rules of the Game, Louis Massicotte, André Blais, and Antoine Yoshinaka provide the first thorough examination of these laws. The study incorporates original data collected from more than sixty democracies around the world, and touches on oft-ignored, yet extremely important, aspects of election laws. The countries covered by the study include Argentina, Brazil, Canada, France, Japan, the Netherlands, the Philippines, Romania, and the United Kingdom. The authors focus on six dimensions of election laws: the right to vote, the right to be a candidate, the electoral register, the agency in charge of the election, the procedure for casting votes, and the procedure to sort out the winners and losers.Massicotte, Blais, and Yoshinaka uncover underlying patterns, explaining why certain types of country tend to adopt a given sets of rules. In general, former colonies adopt the same laws as their former mother country. There is also a tendency for established democracies to be more inclusive than non-established ones. The authors point out sociological patterns and review normative and practical arguments for and against each set of rules, providing invaluable information for students of elections and democratic theory as well as election practicioners
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".