Gendering Diplomatic Careers. Distance and Time in International Assignment Practices Among 600 French Diplomats
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".