MétaCan
Menu
Back to cohort
Record W4402189011 · doi:10.32920/26883745

Obscure, Minimize, and Distract: The Canadian State's Playbook on the Characterization and Justification of Immigration Detention

2024· preprint· en· W4402189011 on OpenAlexaffabout
Connie Lam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsImmigration detentionImmigrationPolitical scienceState (computer science)LawCriminologyLaw and economicsPsychologySociologyComputer scienceAlgorithm

Abstract

fetched live from OpenAlex

<p>State discourse on immigration detention reveals a concerted effort to obscure the realities of immigration detention in Canada. This paper exposes Canadian statutes and federally published websites as non-neutral domains where discursive violence takes place and where enactments of state violence are justified. Although it positions itself as a welcoming 'safe haven' for migrants, the Canadian state distracts us from the punitive nature of indefinite immigration detention, minimizes the number of migrants detained, and inflates the danger that migrants pose to Canadian society and borders. The Canadian state's moral authority to enforce immigration detention must not be accepted as natural, lest we naturalize the violence, harm, and settler domination that the state enacts against 'undesirable' migrants. Applying a Critical Discourse Analysis, this paper investigates Canadian state and state agency discourses on/of immigration detention in order to reveal the discursive strategies the state employs to characterize and justify immigration detention. </p>

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.287
Teacher spread0.242 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicEuropean Criminal Justice and Data ProtectionFrench-language works237,207