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Record W3123497234 · doi:10.1257/aer.103.2.624

Inferring Strategic Voting

2013· article· en· W3123497234 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Economic Review · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGame Theory and Voting Systems
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsVotingCardinal voting systemsEconomicsEconometricsDisapproval votingEstimatorBullet votingRanked voting systemApproval votingFirst-past-the-post votingAnti-plurality votingMicroeconomicsPolitical scienceStatisticsMathematicsPolitics

Abstract

fetched live from OpenAlex

We estimate a model of strategic voting and quantify the impact it has on election outcomes. Because the model exhibits multiplicity of outcomes, we adopt a set estimator. Using Japanese general-election data, we find a large fraction (63.4 percent, 84.9 percent) of strategic voters, only a small fraction (1.4 percent, 4.2 percent) of whom voted for a candidate other than the one they most preferred (misaligned voting). Existing empirical literature has not distinguished between the two, estimating misaligned voting instead of strategic voting. Accordingly, while our estimate of strategic voting is high, our estimate of misaligned voting is comparable to previous studies. (JEL D72)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.027

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.

Opus teacher head0.050
GPT teacher head0.252
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it