Institutionalizing Precarious Immigration Status in Canada
Bibliographic record
Abstract
This paper analyzes the institutionalized production of precarious migration status in Canada. Building on recent work on the legal production of illegality and non-dichotomous approaches to migratory status, we review Canadian immigration and refugee policy, and analyze pathways to loss of migratory status and the implications of less than full status for access to social services. In Canada, policies provide various avenues of authorized entry, but some entrants lose work and/or residence authorization and end up with variable forms of less-than-full immigration status. We argue that binary conceptions of migration status (legal/illegal) do not reflect this context, and advocate the use of ‘precarious status’ to capture variable forms of irregular status and illegality, including documented illegality. We find that elements of Canadian policy routinely generate pathways to multiple forms of precarious status, which is accompanied by precarious access to public services. Our analysis of the production of precarious status in Canada is consistent with approaches that frame citizenship and illegality as historically produced and changeable. Considering variable pathways to and forms of precarious status supports theorizing citizenship and illegality as having blurred rather than bright boundaries. Identifying differences between Canada and the US challenges binary and tripartite models of illegality, and supports conducting contextually specific and comparative work.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".