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Record W4400424979 · doi:10.29173/crossings285

Masculinities in Immigration Detention Centres

2024· article· en· W4400424979 on OpenAlexaffabout
Haven Rice

Bibliographic record

VenueCrossings An Undergraduate Arts Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImmigration detentionImmigrationCriminologyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

A great tool to grow an empire, detention camps have been historically used and abused as a subordination tactic by all of the most powerful empires, most notably by Germany’s Nazi Regime in the Second World War. Commonly thought to have been born and died in history, they are a mostly forgotten piece of the past. Many would be shocked to discover that there is a secret world of institutions that disregard international human rights law in order to prioritize their national goals, where refugees, asylum seekers and migrants are the primary victims. Systems for migrancy and immigration detention have emerged in the wake of globalization, seeking asylum from conflicts, natural disasters or financial insecurity, or simply searching for better economic opportunities, education or reunion with family. Multicultural liberal democracies use detention centres to enforce their racialized and gendered penal power and establish a national hierarchy where poor, young men of colour are the most marginalized. This paper critically examines the victimization of male refugees, asylum seekers and migrants by neo-colonial masculinities in Canadian, American and British immigration detention centres and how these experiences create offenders through the victim-offender overlap.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0040.001
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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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