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Record W6940203179 · doi:10.7910/dvn/swipbz

Replication Data for: Ignoring Women’s Performance: A Survey Experiment on Policy Implementation in Argentina

2024· dataset· en· W6940203179 on OpenAlexaff

Bibliographic record

VenueHarvard Dataverse · 2024
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPerspective (graphical)Government (linguistics)Context (archaeology)Distribution (mathematics)Survey data collectionReplication (statistics)

Abstract

fetched live from OpenAlex

How does politician gender shape citizen responses to performance in office? Much of the existing literature suggests that voters hold higher expectations of women politicians and are therefore more likely to punish them for malfeasance. An alternative perspective suggests that voters view men politicians as more agentic and are therefore more responsive to their performance, whether good or bad. Using an online survey experiment in Argentina, we randomly assign respondents to information that the distribution of a government food program in a hypothetical city is biased or unbiased, and we also randomly assign the gender of the mayor. We find that respondents are more responsive to performance information—both positive and negative—about men mayors. We find little evidence that respondents hold different expectations of malfeasance by men versus women politicians. These results contribute to our understanding of how citizens process performance information in a context with few women politicians.

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.

How this classification was reachedexpand

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.029
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.038

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 head0.052
GPT teacher head0.325
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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