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Record W4386332320 · doi:10.18584/iipj.2023.14.2.14843

Client Perceptions of an FASD-Informed Indigenous Restorative Justice Program

2023· article· en· W4386332320 on OpenAlexvenueno aff
Katherine Flannigan, Benjamin Rollans, Mélissa Tremblay, Sandra Potts, Teresa O’Riordan, Carmen Rasmussen, Jacqueline Pei

Bibliographic record

VenueInternational Indigenous Policy Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRestorative justicePsychological interventionPerceptionEconomic JusticeVulnerability (computing)PsychologyCoping (psychology)Social psychologyCriminologyPolitical scienceClinical psychologyLaw

Abstract

fetched live from OpenAlex

Individuals with fetal alcohol spectrum disorder (FASD) can experience multiple layers of adversity that increase vulnerability to justice involvement. Given the systemic overrepresentation of Indigenous Peoples in the justice system, community-based interventions are important for supporting Indigenous individuals with FASD who are justice-involved, yet little is known about individual experiences with such interventions. In this community-based study, we conducted interviews with 12 adults in an FASD-informed Indigenous justice program, revealing stories of coping, growth, and hope. Findings suggest that blending FASD assessment with restorative justice approaches can contribute to physical, human, family/social, and community/cultural resources that support wellbeing. We describe tangible strengths and processes to leverage in practice and policy for supporting justice-involved individuals with FASD across settings and communities.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.399
Teacher spread0.371 · 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 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
Published2023
Admission routes1
Has abstractyes

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