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Record W3090788706 · doi:10.14288/acme.v19i2.1932

Challenging Un-Belonging and Undesirability

2019· article· en· W3090788706 on OpenAlexaffabout
Kathryn Tomko Dennler

Bibliographic record

VenueOpen Collections · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsYork University
Fundersnot available
KeywordsAgency (philosophy)ImmigrationLegitimacyAutonomyHarmRefugeeConstruct (python library)SociologyQualitative researchPolitical sciencePoliticsSocial scienceLaw

Abstract

fetched live from OpenAlex

Increasing processing times for immigration applications, increasing numbers of people admitted on temporary visas, and delays processing refugee claims mean that more newcomers spend longer periods of time living in Canada with precarious immigration status. This paper uses qualitative research to examine how people with precarious immigration status exercise agency in the face of restrictions to their rights and risk of deportability, as well as the extent to which agency is able to transform people’s everyday realities. The research shows that regimes of immigration control construct people with precarious immigration status as un-belonging and undesirable as members of Canadian society. The research identifies two ways that research participants exert autonomy over their lives: persistent presence and critiquing their construction as un-belonging and undesirable. Both forms of agency involve the creation of counterpublics to build networks for practical support and recognition of the legitimacy of their presence in Canada. While agency made it easier for participants to sustain themselves, the research shows that participants internalized discourses hostile to people with precarious immigration status, suggesting that agency is both necessary but also limited in its capacity to mitigate the harm caused by the construction of them as un-belonging and undesirable.

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)0.010
metaresearch head score (Gemma)0.013
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.048
Scholarly communication0.0090.005
Open science0.0010.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.301
Teacher spread0.280 · 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
Published2019
Admission routes2
Has abstractyes

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