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Record W2341109636 · doi:10.1177/1462474515593415

Special issue: Punishment, values and local cultures

2015· article· en· W2341109636 on OpenAlexaboutno aff
Hilde Tubex, David A. Green

Bibliographic record

VenuePunishment & Society · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPenologyImprisonmentPunitive damagesPunishment (psychology)PopulationSociologyNarrativePower (physics)State (computer science)Political scienceCriminologyPolitical economyLawPrisonSocial psychologyPsychology

Abstract

fetched live from OpenAlex

This special issue contributes to a discussion that has become central to contemporary comparative penology: how best to explain convergences and divergences between Western states in their deployment of penal power. Its particular focus is on characteristics of local cultures and the underlying and distinct historical values that best account for jurisdictional particularities in penal policy and outcomes. The recent penal history of post-industrial societies is well described in a set of overarching, global narratives, including, for instance, those that describe and interpret the myriad consequences of the arrival of ‘late-modernity’ (e.g. Garland, 2001), as well as others that focus most on the ways in which the expansion of neo-liberal thinking and policy, and the withering of welfare states, have shaped justifications for and methods of state punishment and social control (e.g. Wacquant, 2009). However, the forces at work in these master narratives manifest differently at the national and jurisdictional levels. Thus, the punitive patterns and trajectories they shape in each are distinct and culturally embedded (Melossi, 2001), resulting in different penal policies, practices and outcomes. Two articles (by Karstedt and Snacken) in this special issue offer theoretical analyses of penal developments at the international level, while four focus on the changing penal contexts in four countries: the United States, Canada, Sweden and Australia. A comparison of their imprisonment rates per 100,000 of the population – which range from 707 (USA), to 114 (Canada), to 57 (Sweden) – presents widely different impressions of each country. A similarly divergent and variable pattern can be observed among the four selected Australian jurisdictions whose

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.027
GPT teacher head0.327
Teacher spread0.300 · 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
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

Citations3
Published2015
Admission routes1
Has abstractyes

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