A Model for Managed Migration? Re‐Examining Best Practices in Canada’s Seasonal Agricultural Worker Program
Bibliographic record
Abstract
Abstract This paper situates Canada’s Seasonal Agricultural Worker Program (SAWP) within the policy and scholarly debates on “best practices” for the management of temporary migration, and examines what makes this programme successful from the perspective of states and employers. Drawing on extensive qualitative and quantitative study of temporary migration in Canada, this article critically examines this seminal temporary migration programme as a “best practice model” from internationally recognized rights‐based approaches to labour migration, and provides some additional best practices for the management of temporary labour migration programmes. This paper examines how the reality of the Canadian SAWP measures up, when the model is evaluated according to internationally recognized best practices and migrant rights regimes. Despite all of the attention to building “best practices” for the management of temporary or managed migration, it appears that Canada has taken steps further away from these and other international frameworks. The analysis reveals that while the Canadian programme involves a number of successful practices, such as the cooperation between origin and destination countries, transparency in the admissions criteria for selection, and access to health care for temporary migrants; the programme does not adhere to the majority of best practices emerging in international forums, such as the recognition of migrants’ qualifications, providing opportunities for skills transfer, avoiding imposing forced savings schemes, and providing paths to permanent residency. This paper argues that as Canada takes significant steps toward the expansion of temporary migration, Canada’s model programme still falls considerably short of being an inspirational model, and instead provides us with little more than an idealized myth.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".