Bibliographic record
Abstract
Canada's immigration policy radically shifted under Stephen Harper's federal Conservative Party government, which ruled from 2006 to 2015. The Temporary Foreign Worker Program (TFWP) is one key example of how migrants are increasingly entering Canada through a racially structured hierarchy of citizenship that privileges whiteness, while increasing the precarity of racialized migrants as they live, work, and contribute to the Canadian economy. This chapter offers a detailed policy analysis of Canada's TFWP, focusing on how the program marginalizes migrant workers as “un-Canadian” by placing them in racial, gender, and class hierarchies of belonging. This paper will discuss and outline recent changes and developments in Canada's TFWP, specifically those related to migrants classified as ‘lower-skilled' workers. While some labour needs in Canada can be read as truly temporary (for example, where workers were required to construct venues for the 2010 Vancouver Winter Olympic Games or other short-term construction projects), the lack of accountability within the TFWP in Canada has led to some occupations being misleadingly framed as ‘temporary', thereby creating a class of migrant workers who are “permanently temporary.” I will argue that the labeling of racialized migrants as “temporary workers” offers employers a structural incentive to keep wages systematically low and maintain poor working conditions, all couched under a guise of “competitiveness.” In this light, “temporary” work becomes synonymous with low-wage exploitation, and continues to strengthen a historic racist nation-state project in Canada. Further, this paper will argue that giving temporary status to migrant workers, rather than permanent residency, serves to limit access to social rights and services, only deepening their levels of exploitation. Finally, I argue that recent increases in TFWs is a symptom of a global trend towards the neoliberalization of citizenship, which has seen the unethical individualization of rights and the privatization of services across many fields.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.054 |
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".