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Record W4399303031 · doi:10.1093/jdsade/enae021

Barriers Experienced by First Nations Deaf People in the Justice System

2024· article· en· W4399303031 on OpenAlexaffabout
Brent C. Elder, Karen Soldatić, Michael Schwartz, Jody Barney, Damien Howard, Patrick McGee

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2024
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersWestern Sydney UniversitySyracuse University
KeywordsInterpreterCriminal justiceEconomic JusticeSign languageCriminologyPsychologySociologyPublic relationsLawPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

Anecdotal evidence strongly suggests that members of the First Nations Deaf community experience more barriers when engaging with the criminal justice system than those who are not deaf. Therefore, our purpose for writing this article is to highlight legal and policy issues related to First Nations Deaf people, including perspectives of professionals working with these communities, living in Australia who have difficulty in accessing supports within the criminal justice system. In this article, we present data from semi-structured qualitative interviews focused on four key themes: (a) indefinite detention and unfit to plead, (b) a need for an intersectional approach to justice, (c) applying the maximum extent of the law while minimizing social services-related resources, and (d) the need for language access and qualified sign language interpreters. Through this article and the related larger sustaining project, we seek to center the experiences and needs of First Nations Deaf communities to render supports for fair, just, and equitable access in the Australian criminal justice system to this historically marginalized group.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.376
Teacher spread0.349 · 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 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

Citations2
Published2024
Admission routes2
Has abstractyes

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