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Record W4254097697 · doi:10.32920/ryerson.14657223.v1

Constructing the nation through immigration law and legal processes of belonging

2021· preprint· en· W4254097697 on OpenAlexaffabout
Valerie Molina

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsSubjectivityIdeologyCitizenshipImmigrationImmigration lawOpposition (politics)Political scienceNational identityNationalismImmigration policyPolitical economyIdentity (music)SociologyLawGender studiesPolitics

Abstract

fetched live from OpenAlex

The aim of this critical literature review is to define the connection between immigration policies and the construction of a national identity, and to discuss what the implications of such connections may be. Tracing how the legal subjectivity of the migrant has developed throughout time and through policy reveals how messages about the nation and Others are created, sustained, and circulated through legal policies. What values are implicit within Canadian immigration policy? How does the migrant ‘other’ help ‘us’ stay ‘us’? How do nationalist ideologies construct the Other and how is this reflected in labour market segmentation? Constructing a national identity involves categorizing migrants into legal categories of belonging, a process in which historical positions of power are both legitimized and re-established through law. Discourses about temporary foreign workers provide examples of how the Other is framed in limited terms and in opposition to that of legitimate members of Canadian society. Key Terms: Citizenship, discourse, subjectivity, immigration law, identity, power, humanitarianism, temporary foreign workers, labour market segmentation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.084
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0100.054
Scholarly communication0.0140.013
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.304
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2021
Admission routes2
Has abstractyes

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