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Record W7097690900

© The author(s), 2009 | Licensed to the Surveillance Studies Network under a Creative Commons Attribution Non-Commercial No Derivatives license.

2016· article· en· W7097690900 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)InvocationInclusion (mineral)InstitutionCoercion (linguistics)NewspaperAttributionEntitlement (fair division)
DOInot available

Abstract

fetched live from OpenAlex

This paper begins with Foucault’s definition of the ‘carceral ’ as an incorporation of “institutions of supervision or constraint, of discreet surveillance and insistent coercion ” (1995, 299) to examine how the invocation of the War Measures Act during World War II translated into virtual incarceration for Japanese Canadians. Using newspaper articles from a one-year period, I apply this definition to Japanese Canadians sent 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, media and civic mediations. Moving beyond the carceral institution to interrogate less formal spaces of carcerality, this paper strives to build on Foucault’s definition through the inclusion of broader, less bounded and less definitive spaces.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.598
Threshold uncertainty score0.853

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4020.170

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 head0.050
GPT teacher head0.335
Teacher spread0.285 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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Same topicJapanese History and CultureFrench-language works237,207