Language at Work: The Impact of Linguistic Enclaves on Immigrant Economic Integration
Bibliographic record
Abstract
This paper studies the role played by linguistic enclaves on the economic integration of immigrants to Canada. Linguistic enclaves are defined as groups of people who are similar with respect to languages used on their jobs. A five category classification of major types of linguistic enclaves is produced, using responses to two questions on the Canadian 2006 census of population: language most often used on the job and language(s) regularly used at work. Two core questions are asked: 1) What factors influence the likelihood of employment in linguistic enclaves; and 2) What are the impacts of working in linguistic enclaves on earnings? These questions are answered by examining the economic integration of immigrant allophone women and men age 26-64 who were employed in 2005 or 2006 and who were enumerated in the 2006 Canadian census of population. The investigation shows that levels of language proficiency are important factors determining the type of language enclave where individuals are employed. Further language at work mediates much of the observed impacts of language proficiency on earnings. Wage determination models also confirm that employment in linguistic enclaves conditions weekly earnings; allophone immigrants who use non-official languages at work have lower wages than those who use only English at work.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".