De La Búsqueda De Empleo Al Ascenso: Experiencias De Las Minorías Étnicas En Pekín (From Job Search to Hiring to Promotion: The Labour Market Experiences of Ethnic Minorities in Beijing)
Bibliographic record
Abstract
Valiendose de datos del censo y de entrevistas personales, el autor estudia las experiencias laborales de los trabajadores y solicitantes de empleo de Pekin pertenecientes a minorias etnicas. Los datos del mercado de trabajo indican que estas personas se hallan en desventaja respecto de la etnia han, que es la dominante, sobre todo en el acceso a los puestos mas cualificados y mejor remunerados. Las comprobaciones del autor dejan entrever que ello puede achacarse a las lagunas de que adolece el orden laboral y que fomentan la dependencia de las redes sociales en este terreno.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=2211233Winner 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".