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Record W4211247750 · doi:10.1017/9781316875544

Red Zones

2020· book· en· W4211247750 on OpenAlexaff
Marie-Ève Sylvestre, Nicholas Blomley, Céline Bellot

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversité de MontréalSimon Fraser UniversityUniversity of Ottawa
Fundersnot available
KeywordsLegislationPunishment (psychology)Criminal justiceContext (archaeology)Political scienceCriminologyEconomic JusticeLawCriminal lawSociologyGeographyPsychologyArchaeology

Abstract

fetched live from OpenAlex

In Red Zones, Marie-Eve Sylvestre, Nicholas Blomley, and Céline Bellot examine the court-imposed territorial restrictions and other bail and sentencing conditions that are increasingly issued in the context of criminal proceedings. Drawing on extensive fieldwork with legal actors in the criminal justice system, as well as those who have been subjected to court surveillance, the authors demonstrate the devastating impact these restrictions have on the marginalized populations - the homeless, drug users, sex workers and protesters - who depend on public spaces. On a broader level, the authors show how red zones, unlike better publicized forms of spatial regulation such as legislation or policing strategies, create a form of legal territorialization that threatens to invert traditional expectations of justice and reshape our understanding of criminal law and punishment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.423
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.241
Teacher spread0.210 · 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 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

Citations29
Published2020
Admission routes1
Has abstractyes

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