MétaCan
Menu
Back to cohort
Record W2764788230 · doi:10.14452/mr-067-05-2015-09_5

Stripping Away Invisibility: Exploring the Architecture of Detention

2015· article· en· W2764788230 on OpenAlexaboutno aff
Victoria Law

Bibliographic record

VenueMonthly Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPrisonInvisibilitySociologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

<div class="bookreview">tings chak, <em>Undocumented: The Architecture of Migrant Detention</em> (Montreal: Architecture Observer, 2014), 112 pages, 22 euros ($30.60 from Amazon), paperback.</div> Over the past six years, more than 100,000 people, including children, have been jailed in Canada, many without charge, trial, or an end in sight, merely for being undocumented.… Locked away from the public eye, they become invisible.… Like the people within, immigrant detention centers are often invisible as well. Photos and drawings of these places are rarely public; access is even more limited. Canada has three designated immigrant prisons, and it also rents beds in government-run prisons to house over one-third of its detainees.… <em>Undocumented: The Architecture of Migrant Detention </em>begins to strip away at this invisibility. In graphic novel form, Toronto-based multidisciplinary artist tings chak draws the physical spaces of buildings in which immigrant detainees spend months, if not years. In crisp black and white lines, chak walks the reader through the journey of each of these 100,000+ people when they first enter an immigrant detention center.<p class="mrlink"><p class="mrpurchaselink"><a href="http://monthlyreview.org/index/volume-67-number-5" title="Vol. 67, No. 5: October 2015" target="_self">Click here to purchase a PDF version of this article at the <em>Monthly Review</em> website.</a></p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.162
GPT teacher head0.352
Teacher spread0.190 · 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 designOther design
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMonthly ReviewSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207