Bibliographic record
Abstract
Abstract Participation in ethnic economies has been regarded as an alternative avenue of economic adaptation for immigrants and minorities in major immigrant‐receiving countries. This study examines one important dimension of ethnic economies: co‐ethnic concentration at the workplace. Using a large national representative sample from Statistics Canada’s 2002 Ethnic Diversity Survey, this study addresses four questions: (1) what is the level of co‐ethnic concentration at the workplace for Canada’s minority groups? (2) How do workers who share the same ethnicity with most of their co‐workers differ from other workers in socio‐demographic characteristics? (3) Is higher level of co‐ethnic concentration at the workplace associated with lower earnings? (4) Is higher level of co‐ethnic concentration at the workplace associated with higher levels of life satisfaction? The results show that only a small proportion of immigrants and the Canadian‐born work in ethnically homogeneous settings. In Canada’s eight largest metropolitan areas about 10 per cent of non‐British/French immigrants share a same ethnic origin with the majority of their co‐workers. The level is as high as 20 per cent among Chinese immigrants and 18 per cent among Portuguese immigrants. Among Canadian‐born minority groups, the level of co‐ethnic workplace concentration is about half the level for immigrants. Immigrant workers in ethnically concentrated settings have much lower educational levels and proficiency in English/French. Immigrant men who work mostly with co‐ethnics on average earn about 33 per cent less than workers with few or none co‐ethnic coworkers. About two thirds of this gap is attributable to differences in demographic and job characteristics. Meanwhile, immigrant workers in ethnically homogenous settings are less likely to report low levels of life satisfaction than other immigrant workers. Among the Canadian‐born, co‐ethnic concentration is not consistently associated with earnings and life satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".