18. Interrogating Managed Migration’s Model: A Counternarrative of Canada’s Seasonal Agricultural Workers Program
Bibliographic record
Abstract
Temporary migrant worker programs (TMWPs) for less-skilled workers are on the rise throughout high-income countries, with new programs emerging and older versions experiencing renewed growth.Amid the growing securitization of borders and restrictive immigration policies by high-income states, TMWPs hold their attractiveness.These guest-worker programs seek to solve labor shortages by issuing temporary entry and work permits to migrants from lower-income countries who are offered jobs but not permanent residence.Within policy circles the resurgence of guest-worker policies has been accompanied by a search for models and codes of practices for implementing managed migration programs effectively.Internationally, Canada's Seasonal Agricultural Workers Program (SAWP) has often been regarded as a model.This chapter describes and interrogates the narrative of the model Canadian guest-worker program.Among the questions that it seeks to answer: Why is the Canadian SAWP considered a model TMWP?What evidence contradicts this image?How does this narrative, while contributing to pragmatic solutions within the contemporary immigration policy environment, in practice legitimize discrimination against migrants and the denial of their rights?
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.051 | 0.035 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| 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".