From engineer to taxi driver? Language proficiency and the occupational skills of immigrants
Bibliographic record
Abstract
Abstract We examine the ability of immigrants to transfer the occupational human capital they acquired prior to immigration. We first augment a model of occupational choice to study the implications of language proficiency on the cross‐border transferability of occupational human capital. We then explore the empirical predictions using information about the skill requirements from O * NET and a unique dataset that includes both the last source country occupation and the first four years of occupations in Canada. We supplement the analysis using Census estimates for the same cohort with source country occupational skill requirements predicted using detailed human capital related information such as field of study. We find that male immigrants to Canada were employed in source country occupations that typically require high levels of cognitive skills, but rely less intently on manual skills. Following immigration, they find initial employment in occupations that require the opposite. Consistent with the hypothesized asymmetric role of language in the transferability of previously acquired cognitive and manual skills, these discrepancies are larger among immigrants with limited language fluency.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".