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Record W4406930260 · doi:10.1111/gwao.13233

Gendering Diplomatic Careers. Distance and Time in International Assignment Practices Among 600 French Diplomats

2025· article· en· W4406930260 on OpenAlexafffund
Romain Lecler, Yann Goltrant

Bibliographic record

VenueGender Work and Organization · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsPolitical scienceSociologyDemographic economicsGender studiesEconomics

Abstract

fetched live from OpenAlex

ABSTRACT Over the past few decades, diplomatic organizations have recruited increasing numbers of women as career diplomats. However, research in the fields of both expatriation and diplomacy emphasizes that transnational careers have been historically monopolized by men, that most “trailing spouses” are still women, and that men's transnational careers still take precedence in dual‐career couples. Our study uses the “gender turn” in diplomatic studies to better understand how gender disparities continue to structure diplomatic careers, even as more women take on primary roles in expatriation. We focus on international assignment practices that we redefine as “gendered diplomatic practices”. We present an original random sample of 300 male and 300 female French diplomats employed in 2015. We show that men cover 1.5 times more distance than women throughout their careers, and that they travel 1.3 times further for each international assignment. We also show that men spend 14 years abroad on average, 3 years more than women. In addition, women spend 1 year longer in each assignment to avoid frequent relocation. These gendered disparities hold for diplomats who access managerial positions. They underscore the necessity for ongoing research and efforts to address sex disparities in transnational careers and measure the gendering of diplomatic practices.

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 score0.027
Threshold uncertainty score0.339

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 studies0.0000.000
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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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