Global Economics Ltd. An Analysis of the Poor Performance of Recent Immigrants and Observations on Immigration Policy By
Bibliographic record
Abstract
This paper examines the poor performance of recent immigrants to Canada in the labour market as revealed in the Statistics Canada Census 2006 Public Use Microdata File (PUMF). It presents the data which shows that immigrants from less developed countries are doing much worse than immigrants from industrialized countries. And unlike previous studies, it focuses on why immigrants from particular countries and regions do worse than others, rather on a comparison with non-immigrants. Using regression analysis it shows that key explanatory variable for the poor performance of recent immigrants are their education, their visible minority status, their language skills, their occupations, and their countries of origin. A profiling of immigrants who have done better than non-immigrant Canadians suggests that the performance of immigrants could be improved by utilizing information from the Census on the characteristics of immigrants who succeed in labour markets to improve the selection criteria and distribution of points used in the current scoring system to choose immigrants, but this would leave untouched the problem of the underperformance of immigrants who are not selected under the point system. This paper reaffirms and updates to 2005 our knowledge
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.103 | 0.032 |
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".