‘It’s a reward for good performance’: exploring the role of employer–employee relationships in Canada’s two-step economic immigration system
Bibliographic record
Abstract
This article is based on personal narrative-style interviews with various stakeholders connected to Canada’s Temporary Foreign Worker Programme and delves into the significant role employers play in the two-step immigration system and to the rise of Canada’s Provincial Nominee Programme. The research draws from interviews with employers, settlement workers, union representatives, and government officials, shedding light on the transition from a human capital approach to immigration to an employment-driven system. At present, Canada’s Provincial Nominee Programme serves as the primary pathway for economic-class immigrants to obtain Canadian permanent residence. It is projected that by 2025, one-fifth of all new permanent residents will have been admitted through this programme, highlighting a shifting landscape where immigrant selection is increasingly being delegated from the federal government to other actors, such as provincial governments and employers. The article focuses on how stakeholders employ discretion within Canada’s two-step immigration system and explores the affective dynamics that this discretion produces. The findings illuminate how the delegation of immigrant selection to employers amplifies the importance of intricate human, economic, and administrative relationships. These relationships form complex webs of entanglement and mutual reliance, exerting a significant influence on the outcomes of Canada’s economic immigration process.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".