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Record W4378716921 · doi:10.1080/09687599.2023.2215395

Supporting Indigenous people with disability in contact with the justice system: a systematic scoping review

2023· article· en· W4378716921 on OpenAlexaboutno aff
Corinne Walsh, Stefanie Puszka, Francis Markham, Jody Barney, Mandy Yap, Tony Dreise

Bibliographic record

VenueDisability & Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
FundersDepartment of Social Services, Australian GovernmentAustralian Government
KeywordsAotearoaIndigenousInclusion (mineral)Criminal justiceAgency (philosophy)Economic JusticeGrey literaturePublic relationsBest practiceSystematic reviewDisability studiesPolitical scienceCriminologySociologyPsychologySocial psychologyMEDLINELawSocial science

Abstract

fetched live from OpenAlex

The relationship between race, disability and criminality is complex and poorly understood. Scant information, and lack of action, exists on how to best keep Indigenous people with disability out of the justice system, and support this cohort while in the system. This systematic scoping review collates grey and peer-reviewed literature in Australia, Aotearoa (New Zealand), the United States and Canada, to gain insight into the current practices in place for justice-involved Indigenous people with disability, and list promising principles which may inform future practice. We identified 1,301 sources, and 19 of these met the inclusion criteria. Across these sources, nine key principles emerged: need for Indigenous designed, led and owned approaches; appropriately identify and respond to disability/needs; appropriate court models; appropriate diversionary options; therapeutic, trauma-informed, strengths-based and agency-building responses; facilitate connection to family, community and support networks; break down communication barriers; protect human rights; and provide post-release support.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.028
GPT teacher head0.364
Teacher spread0.335 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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