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Record W3126072627

Gender Quotas and the Crisis of the Mediocre Man: Theory and Evidence from Sweden

2012· preprint· en· W3126072627 on OpenAlexaff
Timothy Besley, Olle Folke, Torsten Persson, Johanna Rickne

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsMeritocracyIncentiveBallotOddsPolitical scienceCompetence (human resources)PoliticsDemographic economicsEmpirical evidenceDemocracyPolitical economyPublic administrationSocial psychologyEconomicsPsychologyVotingMarket economyLawLogistic regression
DOInot available

Abstract

fetched live from OpenAlex

Efforts to increase female political representation are often thought to be at odds with meritocracy. This paper develops a theoretical framework and an empirical analysis to examine this idea. We show how the survival concerns of a mediocre male party leadership can create incentives for gender imbalance and more incompetent men in office. The predictions are tested with data on candidates in Swedish municipalities over seven elections (1988-2010), where we use admin-istrative data on labor-market performance to craete a measure of the competence of politicians. We investigate the effects of the "zipper" quota, requiring party groups to alternate male and female names on the ballot, unilaterally implemented by the Social Democratic party in 1993. Far from being at odds with meritocracy, this quota increased the competence of male politicians where it raised the share of female representation the most. ∗The authors thank seminar participants at Science-Po, Harvard, Stockholm University,

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.111
GPT teacher head0.412
Teacher spread0.301 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2012
Admission routes1
Has abstractyes

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