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Record W633594584 · doi:10.4324/9781843146049-2

Introduction: Transnational and Comparative Criminology in a Global Perspective

2012· book-chapter· en· W633594584 on OpenAlexaboutno aff
James Hardie-Bick, James Sheptycki, Ali Wardak

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)CriminologySociologyPolitical scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Any criminology worthy of the name should contain a comparative dimension. The contents of cultural meanings that are loaded into the subject of criminology are too variable for it to be otherwise. It is fair to say that most of the important points made by leading scholars of criminology are comparative in nature. It is just that the basis of comparison is often relatively narrow. For example, the hyphen that both separates and binds the phrase ‘Anglo-American criminology’ implies an obvious basis for comparison, although it remains within ‘Anglophonia’. British criminology invites comparison between England, Wales, Northern Ireland and Scotland, even if the differences are too often disregarded by criminologists there. Federal systems such as Australia, Canada and the United States offer a good basis for comparative work and European criminology provides ample room for comparison, with added richness due to wide linguistic and national variability. At a real stretch comparative criminology would go for total global reach and try to touch upon matters of criminological concern on all the major populated continents of the world. Such an undertaking is rare indeed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.340
Teacher spread0.230 · 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.

Study designTheoretical or conceptual
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

Citations24
Published2012
Admission routes1
Has abstractyes

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