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Record W2064871171 · doi:10.1111/0162-895x.00227

The Making of Female Presidents and Prime Ministers: The Impact of Birth Order, Sex, of Siblings, and Father‐Daughter Dynamics

2001· article· en· W2064871171 on OpenAlexaff
Blema S. Steinberg

Bibliographic record

VenuePolitical Psychology · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsMcGill University
Fundersnot available
KeywordsDaughterPoliticsBirth orderPopulationPower (physics)PsychologyDemographyAffect (linguistics)Order (exchange)Developmental psychologySocial psychologyPolitical scienceSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Although research into the factors that may affect male achievement of political leadership is relatively robust, very few studies on the making of female presidents and prime ministers exist. This paper examines the literature on birth order, sex of siblings, and parent‐daughter dynamics to see whether the findings for male political leaders—that first‐born individuals will be overrepresented as compared with later‐born siblings—also hold for female ones. Two other hypotheses were tested concerning differences in birth order and sex of siblings between female political leaders and a larger sample of women. A review of the literature on parent‐daughter dynamics suggests that this may be another important variable for future research into explanations for the success of women who achieve senior‐level positions of power. The findings suggest that first‐born women, like first‐born men, are overrepresented among political leaders; that first‐born women are overrepresented among female political leaders as compared with their numbers in a larger sample population; and that fewer female political leaders have an older brother than would be expected to occur in a larger sample population. The last finding applies only for women who come to power in the period 1960–1989, not those who gained office more recently.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.060
GPT teacher head0.447
Teacher spread0.387 · 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 teacher head, 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

Citations27
Published2001
Admission routes1
Has abstractyes

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