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Record W7118408950 · doi:10.65264/cism6634

Article 3: A.R.T. & Justice: An Arts-based initiative to support the holistic health & dignity of incarcerated Indigenous and Non-Indigenous people in western Canada during and beyond COVID-19

2022· article· W7118408950 on OpenAlexaboutno aff
Helen Brown, Kelsey Timler, Dan Jack, Kirsten Sigerson

Bibliographic record

VenueAdvancing Corrections Journal · 2022
Typearticle
Language
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousMental healthDignityPandemicSocial isolationIsolation (microbiology)Indigenous cultureMental health service

Abstract

fetched live from OpenAlex

COVID-19 has intensified social isolation and worsened mental health for people in Canada and around the world. In response, Correctional Service Canada (CSC) and an academic team launched an artsbased initiative to support the well-being of federally incarcerated Indigenous and non-Indigenous peoples. We gifted art kits across institutions in the Pacific Region and invited individuals to share resulting artwork. Preliminary results show the therapeutic benefits of art-making amidst reduced access to visitation, programs, and Indigenous Elders. We share these initial findings, their policy potential within other international contexts, and discuss how ART-Justice could be transferred and studied through partnerships to mitigate mental health harms and isolation for incarcerated people during the pandemic and beyond.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.375
Threshold uncertainty score0.754

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0160.004
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.003

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 head0.060
GPT teacher head0.360
Teacher spread0.300 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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