Labour Market Information for Employers and Economic Immigrants in Canada: A Country Study
Bibliographic record
Abstract
This report draws lessons from the Canadian immigration experience that can contribute to improving the labour market outcomes of immigrants and alleviate barriers related to labour market information issues. Foreign-born workers often lack the necessary information to learn about opportunities in the Canadian labour market, which can prevent highly-skilled workers from finding employment in their field, to the detriment of the Canadian economy. We examine the services provided to immigrants in Canada by federal and provincial governments, and the large role played by the non-profit sector in facilitating the delivery of information and services to immigrants in order to lessen the informational barriers to immigrant employment. We further identify best practices from Canada, which include establishing national standards for the recognition of foreign qualification; simplifying the delivery of services by using one-stop shops or single-points-of-contact; involving local stakeholders in the development of policy and delivery of service; and maintaining a flexible immigration policy. Identifying and addressing the specific needs of newcomers to Canada has had a strong positive impact on their labour market outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".