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Record W3044756625 · doi:10.1177/2632666320936436

Rethinking confinement through Canada’s alternatives to detention program

2020· article· en· W3044756625 on OpenAlexaffabout
Constantine Gidaris

Bibliographic record

VenueIncarceration · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsContext (archaeology)AutonomyImmigrationImmigration detentionInternet privacySociologyComputer securityPolitical scienceCriminologyPublic relationsComputer scienceLawGeography

Abstract

fetched live from OpenAlex

This article examines Canada’s immigration and detention system through its newly established alternatives to detention (ATD) program. As a program that relies rather extensively on mobile carceral technologies such as electronic monitoring and voice reporting, I argue that we should consider these alternatives not only as extensions of carceral apparatuses and the detention system but as pervasive, far-reaching, and more abstract manifestations of control and confinement. I introduce the term “techno-carcerality” to theorize how we might understand the shift from traditional modes of confinement to less traditional ones, grounded in mobile, electronic, and digital technologies. I contend that the use of mobile carceral technologies in the context of immigration and detention imposes physical, psychological, spatial, and data-driven modes of control and confinement in private, public, and digital space, which are veiled behind notions and misconceptions of increased freedom, mobility, and autonomy. While the ATD program may offer some better options than those available within detention centers or other carceral institutions, it is a program that involves immense surveillance dataveillance techniques that lead to profound carceral effects and anxieties, affecting everyday life in ways that experientially mimic and transcend conventional confinement.

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 score0.902
Threshold uncertainty score0.542

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)0.0000.000

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 head0.056
GPT teacher head0.337
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 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

Citations6
Published2020
Admission routes2
Has abstractyes

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