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Record W4230240117 · doi:10.32920/ryerson.14638770.v1

Institutionalizing Precarious Immigration Status in Canada

2021· preprint· en· W4230240117 on OpenAlexaffabout
Luin Goldring, Carolina Berinstein, Judith K. Bernhard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsToronto Metropolitan UniversityAccess Alliance Multicultural Health and Community ServicesYork University
Fundersnot available
KeywordsCitizenshipImmigrationLegal statusContext (archaeology)ResidenceImmigration policyPolitical sciencePrecarious workWork (physics)SociologyDemographic economicsPolitical economyGeographyLawEconomicsPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.283
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2021
Admission routes2
Has abstractyes

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