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Record W2953221468 · doi:10.1007/978-3-030-16253-5

Homicide and Organised Crime: Ethnographic Narratives of Serious Violence in the Criminal Underworld

2019· book· en· W2953221468 on OpenAlexfundno aff
Mohammed Rahman

Bibliographic record

VenueBCU Open Access Repository (Birmingham City University) · 2019
Typebook
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent UniversityBirmingham City University
KeywordsHomicideCriminologyNarrativeEthnographyViolent crimePolitical scienceMedical emergencySociologyPsychologyPoison controlSuicide preventionMedicineArtAnthropologyLiterature

Abstract

fetched live from OpenAlex

Risk is a major contemporary issue which has widespread implications for theory, policy, governance, public protection, professional practice and societal understandings of crime and criminal justice.The potential harm associated with risk can lead to uncertainty, fear and conflict as well as disproportionate, ineffective and ill-judged state responses to perceived risk and risky groups.Risk, Crime and Society is a series featuring monographs and edited collections which examine the notion of risk, the risky behaviour of individuals and groups, as well as state responses to risk and its consequences in contemporary society.The series will include critical examinations of the notion of risk and the problematic nature of state responses to perceived risk.While Risk, Crime and Society will consider the problems associated with 'mainstream' risky groups including sex offenders, terrorists and white collar criminals, it welcomes scholarly analysis which broadens our understanding of how risk is defined, interpreted and managed.Risk, Crime and Society examines risk in contemporary society through the multi-disciplinary perspectives of law, criminology and socio-legal studies and will feature work that is theoretical as well as empirical in nature.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.001
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 head0.054
GPT teacher head0.341
Teacher spread0.287 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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