Recherche D’Emploi, Embauche Et Promotions Le Vécu Des Minorités Ethniques Sur Le Marché Du Travail De Beijing (From Job Search to Hiring to Promotion: The Labour Market Experiences of Ethnic Minorities in Beijing)
Bibliographic record
Abstract
S'appuyant sur les microdonnees d'un recensement et les entretiens qu'il a menes aupres de travailleurs et d'employeurs, l'auteur examine la recherche d'emploi, l'embauche et la promotion des personnes issues des minorites ethniques a Beijing. Les donnees indiquent que les minorites ethniques sont defavorisees par rapport a l'ethnie dominante Han, notamment en ce qui concerne les emplois qualifies offrant un salaire eleve. Les entretiens individuels menes par l'auteur montrent que ce handicap peut etre du aux failles de la structure institutionnelle qui favorisent le recours aux reseaux sociaux lors de la recherche d'emploi, de l'embauche et des promotions.Drawing on micro-level census data and interviews with individual workers and employers, this article examines the job-search, hiring and promotion experiences of ethnic minority workers and job seekers in Beijing. Labor market data indicate that ethnic minorities are at a disadvantage relative to the dominant Han ethnic group, particularly when it comes to employment in high-wage, skilled jobs. The evidence provided here suggests this may be attributable to gaps in the institutional framework that encourage reliance on social-network capital for job search, hiring and promotion.The English version of this paper can be found at:http://ssrn.com/abstract=2211233 Winner of the Society for the Study of Social Problems’ Poverty, Class, and Inequality Division Paper Award.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".