The Big Payoff? Educational and Occupational Attainments of Ethnic Minorities in Beijing
Bibliographic record
Abstract
Ethnic minority development in Beijing has been marred by deep-seated historical experiences of strained ethnic relations. In spite of this situation, this article demonstrates that ethnic minorities in the capital city have achieved greater educational attainments than the dominant, Han group. Yet, when it comes to their occupational outcomes in high-wage, education-intensive (HWEI) sectors, minorities seemingly pay an 'ethnic penalty'. That is, the Han demographic are disproportionately represented in HWEI occupational sectors. Building upon previous evidence, this article discusses this discrepancy and offers suggestions for improvement. Le développement de minorités ethniques dans Pékin a été marqué par un long passé de relations ethniques tendues. Malgré cette situation, cet article démontre que les minorités ethniques dans la capitale réussissent à atteindre un niveau d'éducation plus élevé que le groupe Han dominant. Pourtant en ce qui concerne leur réussite professionnelle dans les secteurs à qualification et salaire élevés (HWEI), les minorités semblent payer une « pénalité ethnique ». Autrement dit, d'un point de vue démographique, les Han sont sur représentés dans les secteurs professionnels HWEI. Se référant à des études existantes, l'article examine ce décalage et suggère des solutions pour améliorer la situation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".