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Record W225354914

Neo-Colonial Criminology: Quantifying the Silence

2014· article· en· W225354914 on OpenAlexaboutno aff
Antje Deckert

Bibliographic record

VenueAfrican journal of criminology and justice studies · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologyIndigenousEliteCriminal justiceMainstreamSociologyContext (archaeology)Mass incarcerationSilenceSubject (documents)State (computer science)Gender studiesSocial sciencePolitical scienceLawPoliticsHistoryEcology
DOInot available

Abstract

fetched live from OpenAlex

In Australia, Canada, New Zealand and the United States of America, Indigenous peoples continue to experience incarceration at markedly disproportionate rates. Some scholars have criticised criminology for contributing to this social problem by marginalising Indigenous peoples in research and research publications. This study is a first attempt to quantitatively evaluate the (de)colonised state of contemporary criminology. It involves a comprehensive review of research on 'Indigenous peoples in the criminal justice context', which has been undertaken in aforesaid countries and was published in elite criminology journals over the past decade (2001-2010). The findings reveal that publication rates on the subject are low both compared to incarceration rates and compared to the quantity of academic discourse about other disproportionately incarcerated social groups. Since an adequate, i.e. attention-grabbing, quantity of academic discourse has been linked to the public recognition of social problems, the dearth of publications on the subject suggests that mainstream criminology inhibits public attention to the issue and thus contributes to the marginalisation of Indigenous peoples, the reproduction of social inequality and the preservation of elite power.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.170
GPT teacher head0.342
Teacher spread0.171 · 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 designObservational
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

Citations31
Published2014
Admission routes1
Has abstractyes

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