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Record W3008660424 · doi:10.1177/1065912920906193

Do Voters Judge the Performance of Female and Male Politicians Differently? Experimental Evidence from the United States and Australia

2020· article· en· W3008660424 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

VenuePolitical Research Quarterly · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlamePoliticsAgency (philosophy)Affect (linguistics)Political scienceSocial psychologyStereotype (UML)Presidential systemPsychologyDemographic economicsEconomicsLawSociology

Abstract

fetched live from OpenAlex

Do gender stereotypes about agency affect how voters judge the governing performance of political executives? We explore this question using two conjoint experiments: one conducted in the United States and the other in Australia. Contrary to our expectations, we find no evidence in either experiment to suggest that female political executives (i.e., governors, premiers, and mayors) receive lower levels of credit than their male counterparts for positive governing performance. We do find evidence that female executives receive less blame than male executives for poor governing performance—but only in the U.S. case. Taken together, our findings suggest that the stereotype of male agency has only a limited effect on voters’ retrospective judgments. Moreover, the results indicate that—when performance information is presented in unframed, factual terms—agentic stereotyping by voters does not, in itself, present a serious obstacle to the re-election of women in powerful executive positions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.250
GPT teacher head0.455
Teacher spread0.205 · 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