Earnings of Chinese Immigrants in the Enclave and Mainstream Economy*
Bibliographic record
Abstract
La documentation sur le sujet n'est pas décisive quant à savoir si léconomie ethnique enclavée enregistre un rendement économique comparable chez les travailleurs et les entrepreneurs immigrants à ceux qui se situent dans le courant économique principal. Les auteurs de cette étude utilisent des données du Recensement du Canada de 2001 sur la langue la plus utilisée au travail din de mesurer la participation des immigrants chinois à léconomie enclavée. Après les avoir comparés au capital humain ainsi qu'aux variations liées au travail et au milieu urbain, les résultats démontrent que le rendement est moins élevé chez les hommes et chez les femmes de l'enclave que celui des gens qui se trouvent dans le courant économique principal. Les caractéristiques de la langue et le type de travail qui s'effectue dans léconomie enclavée expliquent pourquoi le rendement y est inférieur à celui obtenu dans le courant économique principal. The literature in inconclusive as to whether the immigrant enclave economy offers returns to immigrant workers and entrepreneurs comparative to those in the mainstream economy. This study uses data from the 2001 Census of Canada on language most often used at work to measure enclave economy participation for Chinese immigrants. The findings show that returns are lower for men or women in the enclave than those in the mainstream economy, respectively, after controlling for human capital, work‐related and urban variations. Language features of and job type in the enclave economy explain why returns are inferior to that of the mainstream economy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".