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Record W2128721790

State crime, the colonial question and Indigenous peoples

2008· article· en· W2128721790 on OpenAlexaboutno aff
Chris Cunneen

Bibliographic record

VenueResearchOnline at James Cook University (James Cook University) · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsnot available
FundersUniversity of Oxford
KeywordsIndigenousColonialismState (computer science)ScholarshipPolitical scienceEconomic JusticeCriminologyTransitional justiceState formationPolitical economySociologyLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this chapter is to consider how our understanding of state crime needs to be mediated through an appreciation of colonial processes. The chapter explores a number of inter-related issues around the question of colonialism, state crime and Indigenous peoples. The historical relationship between Indigenous people and the development of modern nation states raises the problem of the extent to which contemporary liberal democracies like Australia, Canada or the US were founded on processes we would now regard as state crime, and indeed engaged in activities which at the time could have been regarded as unlawful. Further, while there has been considerable literature on transitional justice and processes for reparations in post-conflict societies, this body of scholarship has tended to ignore the extent to which liberal democracies themselves might be considered in need of 'post-conflict' reconciliation and restorative justice. This chapter explores these questions through a discussion of Australia, Canada and the US, although the primary focus is on Australia and its relationship with the continent's Indigenous peoples.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.023
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.242
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations6
Published2008
Admission routes1
Has abstractyes

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