Structural Stratification in Higher Education and the University Origins of Political Leaders in Eight Countries
Bibliographic record
Abstract
Are political leaders “educationally representative” of their electorates? Because almost all national‐level political leaders are university graduates, this question increasingly centers on whether they attended a select number of highly ranked domestic institutions. This study examines international variations in the concentration of universities attended by political leaders by analyzing publicly available information on all 524 national party leaders from the past century in eight countries: Canada, the United States, the United Kingdom, Australia, Japan, France, Germany, and Sweden. Our analyses reveal four major findings. First, the university origins of political elites are most concentrated in the UK and United States, whose higher education systems are highly stratified. Second, British and American leaders were most likely to attend world top‐ranked universities than leaders from any other countries. Third, our results uncover a realignment between elite universities and party ideology in recent decades, as leaders of left‐wing parties become more likely to attend elite universities than their right‐wing counterparts. In conclusion, we theorize connections between higher education and elite recruitment, and suggest directions for future research that can utilize a broader range of nations and societal sectors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".