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Record W4301041066 · doi:10.46692/9781447321781.007

Reconceptualising sentencing and punishment from an Indigenous perspective

2016· other· en· W4301041066 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPunishment (psychology)IndigenousCriminologyPerspective (graphical)SociologyPsychologySocial psychologyComputer scienceEcologyBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Previously we outlined the over-representation of Indigenous peoples in prison in settler colonial societies. In this chapter we examine the sentencing and punishment of Indigenous peoples. We begin by analysing the way non-Indigenous courts have responded to the sentencing of Indigenous people through two contrasting examples of Australia and Canada. We then discuss what are generally referred to as ‘Indigenous sentencing courts’. These courts have developed in different ways in Australia, Canada, NZ and the US, and the scope of their incorporation into mainstream criminal justice systems varies. However, there are commonalities to the extent that the courts take into account some aspects of Indigenous culture when sentencing. The third area we turn our attention to briefly is the development of a distinctly Indigenous approach to justice reinvestment in Australia, and contrast that with the US, where justice reinvestment has largely ignored issues of Indian imprisonment. Finally, we reflect on healing as an Indigenous response to social harm. Essentially existing outside the formal court and correctional systems, healing approaches have grown over recent decades as both an alternative to the philosophical underpinnings of Western punishment, as well as providing practical alternatives to mainstream non-Indigenous correctional policies and practices. We should be clear that we are not interested here in the debate as to whether the courts impose harsher or more lenient sentences on Indigenous peoples. There have been numerous studies of this type focusing on either ‘race’ or Indigeneity in the US (Alvarez and Bachman, 1996; Steffensmeier and Demuth, 2000; Office of Hawaiian Affairs, 2010), Britain (Hood, 1992) and Australia (Gallagher and Poletti, 1998; Bond and Jeffries, 2011). The studies have found various results. Some have methodological flaws or limitations, for example in the variables that are taken into account; and some are theoretically challenged, for example in their understanding of ‘race’ and its broader effects. For an overview of these issues in the US, see Wolpert (1999) and Davis (2003); in New Zealand see Morrison (2009); and in Australia see Cunneen (2006) and Anthony (2013). The view that discrimination in sentencing can be established through a few simple criteria is simplistic: • First, the focus on discrimination or bias is often caught within a binary ‘equality paradigm’, where the standard against which Indigenous people are judged is the treatment of the (white) majority.

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)0.013
metaresearch head score (Gemma)0.014
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: Other · Consensus signal: Other
Teacher disagreement score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0130.040
Scholarly communication0.0100.008
Open science0.0040.011
Research integrity0.0040.007
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 head0.022
GPT teacher head0.358
Teacher spread0.336 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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