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Record W2803709499 · doi:10.1177/1462474518773332

Ambiguous publicities: Cultivating doubt at the intersection of competing genres of risk evaluation in Catalan Prisons

2018· article· en· W2803709499 on OpenAlexaboutno aff
Johanna Römer

Bibliographic record

VenuePunishment & Society · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
FundersWenner-Gren Foundation
KeywordsCatalanAusterityPrisonContext (archaeology)CriminologyLotteryIdeologyBureaucracyMass incarcerationSociologyPunishment (psychology)Punitive damagesPolitical sciencePsychologySocial psychologyLawPoliticsEconomicsGeography

Abstract

fetched live from OpenAlex

Policymakers in Canada and across Europe have largely embraced the creation of post-disciplinary systems of punishment. In the autonomous region of Catalonia, Spain, this meant expanding connections between prisons and communities, expanding the publics a prison serves. At the same time, in part driven by austerity policies, incarceration in Spain and Catalonia has become more punitive and bureaucratic. Actuarial risk assessments introduced in Catalan prisons in 2009 are an example of this type of reform—designed to facilitate the release of low-risk inmates earlier and to control mobility. Drawing on ethnographic research conducted in Catalan prisons from 2012 to 2014, I show how both actuarial and clinical risk evaluation involved therapists’ anticipation of future aggressive acts on the part of inmates. Analyzing risk assessment as a practice and as an ideological frame, I argue that the short-term focus of risk assessments reinforced existing forms of interpreting inmates’ actions that therapists attempted to hold at bay. Risk as an ideological frame in the context of austerity contributes to a form of publicity that can further isolate inmates rather than facilitating the construction of community inside and outside of a rehabilitative prison.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.336
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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2018
Admission routes1
Has abstractyes

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