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Record W2797723177 · doi:10.1177/0964663918767954

Sexual Violence and the Border: Colonial Genealogies of US and Australian Immigration Detention Regimes

2018· article· en· W2797723177 on OpenAlexaboutno aff
Suvendrini Perera, Joseph Pugliese

Bibliographic record

VenueSocial & Legal Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsColonialismImmigrationImmigration detentionCriminologyContext (archaeology)State (computer science)Sexual violencePolitical scienceForeign nationalRace (biology)SociologyGender studiesLawGeography

Abstract

fetched live from OpenAlex

This article is concerned with delineating the material manifestations of state violence, with a particular focus on sexual violence in immigration detention prisons in the context of two settler-colonial nation states: Australia and the United States. It draws its impetus from the projected work of the late sociolegal scholar, Penny Pether, and her outline for a large-scale project on comparative regimes of indefinite detention. In our article, we pursue an exchange between the draft of Pether’s first chapter, ‘Beginning Again’, for her projected book, and elements of a transnational project titled ‘Deathscapes: Mapping Race and Violence in Settler States’ that we initiated in partnership with colleagues in the United States, Canada and the United Kingdom. We track these linkages in order to argue that these similar, if often different, colonial histories both inform and continue to shape contemporary regimes of detention and their reproduction of sexual violence and assault against their captive populations.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.013
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.368
Teacher spread0.327 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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