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Record W2068345285 · doi:10.5194/gh-69-389-2014

Transnational productions of remoteness: building onshore and offshore carceral regimes across borders

2014· article· en· W2068345285 on OpenAlexaff
Alison Mountz, Jenna M. Loyd

Bibliographic record

VenueGeographica Helvetica · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsWilfrid Laurier UniversityBalsillie School of International Affairs
FundersNational Science Foundation
KeywordsMobilitiesPolitical scienceSubmarine pipelineEnforcementRefugeePoliticsGeographyArgument (complex analysis)Political economySociologyLawOceanography

Abstract

fetched live from OpenAlex

Abstract. This article examines transnational framings of domestic carceral landscapes to better understand the relationship between offshore and onshore enforcement and detention regimes. US detention on mainland territory and interception and detention in the Caribbean serves as a case study. While the US domestic carceral regime is a subject of intense political debate, research, and activism, it is not often analyzed in relation to the development and expansion of an offshore "buffer zone" to intercept and detain migrants and asylum seekers. Yet the US federal government has also used offshore interception and detention as a way of controlling migration and mobility to its shores. This article traces a Cold War history of offshore US interception and detention of migrants from and in the Caribbean. We discuss how racialized crises related to Cuban and Haitian migrations by sea led to the expansion of an intertwined offshore and onshore carceral regime. Tracing these carceral geographies offers a more transnational understanding of contemporary domestic landscapes of detention of foreign nationals in the United States. It advances the argument that the conditions of remoteness ascribed frequently to US detention sites must be understood in more transnational perspective.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.010
GPT teacher head0.309
Teacher spread0.299 · 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 designTheoretical or conceptual
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

Citations36
Published2014
Admission routes1
Has abstractyes

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