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Record W2171689001 · doi:10.24908/ss.v7i1.3305

Carceral Ambivalence: Japanese Canadian ‘Internment’ and the Sugar Beet Programme during World War II

2009· article· en· W2171689001 on OpenAlexafffundabout
Shelly Ikebuchi Ketchell

Bibliographic record

VenueSurveillance & Society · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAmbivalenceCitizenshipCoercion (linguistics)LawRelocationSociologyPrisonPolitical scienceWorld War IIPoliticsSocial psychology

Abstract

fetched live from OpenAlex

Liberty is a fundamental marker of citizenship. During World War II, for Japanese Canadians in prisoner-of-war camps the stripping of their liberty was a sure sign of their loss of citizenship. However, for others the distinction between liberty and incarceration was not as clear. Despite the fact that many Japanese Canadians enjoyed the appearance of relative freedom during World War II, for many citizenship was uncertain and liberty was tenuous at best. Ambivalence infused discussions surrounding the relocation of Japanese Canadians to Alberta and Manitoba. This paper highlights the multiple and diverse processes of incarceration that took place amidst this ambivalence. I begin with Foucault’s (1995) definition of the ‘carceral’ as an incorporation of “institutions of supervision or constraint, of discreet surveillance and insistent coercion” (299). Using newspaper articles from a one year period, I apply this definition to Japanese Canadian ‘relocation’ to Alberta and Manitoba as part of the government sponsored Sugar Beet Programme. This program offers a unique perspective, as it was framed as a ‘self-support’ program, thus implying a greater range of freedoms. However, despite illusions of freedom, I argue that what made these sites carceral was a combination of state and civic mediations.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.998

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 studies0.0030.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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 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

Citations4
Published2009
Admission routes3
Has abstractyes

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