Issue 09: Temporary Migration Policy, Trends, and Ontarioâs Economy: 2000-2012
Bibliographic record
Abstract
Ontario is unique when it comes to international migration in Canada. It is the leading province in overall flows, including individuals participating in the temporary foreign workers (TFWs) program. Employers hire TFWs on a contractual basis to work here, and from 2000 to 2012, about 800,000 came to Ontario – representing 40% of Canada’s total TFWs. Despite their growing numbers, economic importance, and the rapidly changing landscape of federal immigration policy, there is little work looking at the Temporary Foreign Worker Program or its economic impact on the province. In this research, we found that employers in specific industries, like agriculture, senior business management, and childcare, tended to hire TFWs and did so through specific parts of the Program. Our preliminary results show that the influx of TFWs was statistically associated with shorter job tenure, higher Employment Insurance receipts, and increases in wages in some jobs, but lower wages in others. These effects are particularly significant in industries with large numbers of TFWs. So while TFWs undoubtedly contribute to Ontario’s overall economic development, more research should be done to understand their specific economic effects on particular industries and demographics. This is especially important given the provincial responsibilities in labour, health and education, which federal immigration policy directly impacts.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".