The Many Faces of Strategic Voting
Bibliographic record
Abstract
The Many Faces of Strategic VotingStrategic voting is classically defined as voting for one's second preferred option to prevent one's least preferred option from winning when one's first preference has no chance.Voters want their votes to be effective, and casting a ballot that will have no influence on an election is undesirable.Thus, some voters cast strategic ballots when they decide that doing so is useful.This edited volume includes case studies of strategic voting behavior in Israel, Germany, Japan, Belgium, Spain, Switzerland, Canada, and the United Kingdom, providing a conceptual framework for understanding strategic voting behavior in all types of electoral systems.The classic definition explicitly considers strategic voting in a single race with at least three candidates and a single winner.This situation is more common in electoral systems that have single-member districts that employ plurality or majoritarian electoral rules and have multiparty systems.Indeed, much of the literature on strategic voting to date has considered elections in Canada and the United Kingdom.This book contributes to a more general understanding of strategic voting behavior by taking into account a wide variety of institutional contexts, such as single transferable vote rules, proportional representation, two-round elections, and mixed electoral systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".