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Record W2987051512 · doi:10.3390/socsci8110310

Subterranean Detention and Sanctuary from below: Canada’s Carceral Geographies

2019· article· en· W2987051512 on OpenAlexaffabout
Jen Bagelman, Sasha Kovalchuk

Bibliographic record

VenueSocial Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsImmigration detentionContext (archaeology)PoliticsScholarshipSociologyImmigrationCriminologyPolitical scienceGender studiesLawGeographyArchaeology

Abstract

fetched live from OpenAlex

This paper begins with an account of Lucía Vega Jimenez, a Mexican woman who lived and worked in Metro Vancouver, Coast Salish Territories (Canada) and who died while held in detention in British Columbia’s Immigration Holding Centre. This article argues that Lucía’s story exposes a number of critical aspects regarding the geographies and politics of migration in Canada today. First, Lucia’s story points to the ways in which Canada’s determination process invisibilises certain forms of violence and, as such, serves as a highly restrictive and exclusionary mechanism. Second, it shows how this exclusionary mechanism extends like ‘capillaries’ throughout urban space. In this context city services (like transit) increasingly become less spaces of refuge, and more privatized border checkpoints. Third, following Lucia’s story reveals how city checkpoints funnel people with precarious status into remote detention, akin to Foucault’s ‘carceral archipelago.’ While expanding on carceral literature, this paper departs from existing scholarship that tends to think about remoteness horizontally. The paper argues that it is below the surface where carceral regimes become particularly hostile and—as such—the paper calls for deepened engagement with questions of verticality. Finally, the article illustrates how subterranean carceral dimensions are being politicized, agonistically, through sanctuary practices.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

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 studies0.0020.001
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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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.

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

Citations4
Published2019
Admission routes2
Has abstractyes

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