MétaCan
Menu
Back to cohort
Record W2083970410 · doi:10.3138/cjccj.48.5.703

Governing on the Margins: Exploring the Contributions of Governmentality Studies to Critical Criminology in Canada

2006· article· en· W2083970410 on OpenAlexaffvenueabout
James W. Williams, Randy K. Lippert

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of WindsorYork University
Fundersnot available
KeywordsGovernmentalityOppressionCorporate governanceSociologyState (computer science)CriminologyCritical theoryPoliticsShadow (psychology)Power (physics)Political scienceLaw and economicsLawEconomics

Abstract

fetched live from OpenAlex

Despite the promise of the 1970s, critical criminology's influence in Canada has diminished in recent years. This paper examines this decline and charts one possible avenue for renewal. It argues that critical criminology has been limited by its emphasis on the state, and state-centred constructions of criminality, and by its failure to come to terms with how social injustices are reproduced through private institutions and modes of expertise constituted on the margins of the state and in the shadow of the law. Based on this critique, it is proposed that a dialogue with governmentality studies may help to overcome these limits, a dialogue that is examined in two substantive contexts: the governance of immigration and the policing of financial disorder. Revealed are not only forms of governance and oppression enacted on law's margins, but also possibilities for the realization of the progressive politics that lies at the heart of the critical criminological enterprise.

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)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.839

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.008
Science and technology studies0.0410.067
Scholarly communication0.0180.006
Open science0.0030.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.149
GPT teacher head0.343
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207