Shrink the Temporary Foreign Worker Program (TFWP): How a more targeted TFWP can better protect workers in Canada
Bibliographic record
Abstract
Canada’s Temporary Foreign Worker Program (TFWP) is at a crossroads. For much of the 2000s, the TFWP went through a period of significant expansion, and the number of Temporary Foreign Workers (TFWs) in Canada more than tripled between 2002 and 2012. However, it expanded in a way that was not consistent with Canada’s labour market needs, and program design did not create sufficient incentives for employers to search for Canadian workers before hiring TFWs. As a result, it produced adverse effects on the labour market, including wage suppression, increased unemployment, reduced interregional labour mobility, poor working conditions for TFWs, and underemployment of immigrants. This prompted the government to introduce reforms in 2013 and 2014 meant to return the TFWP to its original purpose and reduce the number of TFWs in Canada, particularly in the low-skill stream. However, many groups are pressuring government to move back to a more expanded TFWP, citing serious labour shortage concerns. Amid the pressure, the current government has undertaken a Parliamentary review of the TFWP, planned for release in September. In light of this review and likely program changes to follow, this paper recommends that Canada maintain a limited TFWP that is flexible enough to respond when businesses are facing genuine labour shortages, but that does more to protect Canada’s labour market. Additional measures should be taken to properly target the TFWP, including more rigorous criteria for determining labour shortages and the need for TFWs, higher fees to incentivize employers to do more to hire domestic workers and reduce dependency on TFWs, and better rights and protections for TFWs, including sector- or occupation-specific work permits and a path to residency. The government should also continue to reduce the number of low-skilled TFWs in Canada. Finally, the TFWP needs to be part of a wellcoordinated strategy to capitalize on available domestic labour, including policies that encourage Canadians to take up available jobs. This last point has been largely forgotten in recent versions of the TFWP and is crucial to ensuring that the TFWP protects Canadian workers and reduces dependency on TFWs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".