Bibliographic record
Abstract
Establishing the Rules of the Game: Election Laws in Democracies, Louis Massicotte, André Blais and Antoine Yoshinaka, Toronto: University of Toronto Press, 2004, pp. 191 Whatever one may think of the 2000 American presidential election, it did have one salutary effect: it drew worldwide attention to the importance of fair and impartially applied election laws. The authors of this work needed no such wake-up call; André Blais and Louis Massicotte enjoy a well-deserved international reputation for expertise in this arcane field. But it is likely that their new book, a compendium and analysis of election laws in 63 countries, will attract wider notice because of recent events in the United States. Unfortunately (though understandably), the extreme decentralization and complexity of American election laws prevented the authors from including the U.S. in their comparative database. Happily, the remaining countries in the sample offer more than enough food for thought. The field of election law has been sadly neglected by political scientists and legal scholars (outside the United States); if interest in the topic continues to grow over the coming years, this book should help to nurture a flourishing academic debate.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".