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Record W3134748578 · doi:10.1177/2043610621995838

heART space: Curating community grief from overdose

2021· article· en· W3134748578 on OpenAlexaff
Marion Selfridge, J.C. Robinson, Lisa M. Mitchell

Bibliographic record

VenueGlobal Studies of Childhood · 2021
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGriefHarm reductionSpace (punctuation)HonourParticipatory action researchSociologyPsychologyPublic relationsMedicineNursingPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

This article details the transformation of an empty store into a gallery honouring youth and others who have passed away from overdoses, and the creation of extensive harm reduction and grief support programming that accompanied the display of artwork. The outpouring of community interest, participation, and emotion that surfaced around heART space clearly shows how art, exhibitions and creative programming can help foster communities of care during times of crisis. Drawing from research into practices of care from harm reduction work, grief studies and participatory arts and curatorial studies, the authors explore how heART space comforted youth and others with direct experiences with overdose and disenfranchised grief while creating dialogues with visitors about the stigma of drug use and homelessness. The authors argue curating heART space produced an opportunity for community healing while nuancing and humanizing the way we see people who use drugs. As such, this youth-driven community project created a safe space to share stories, collaborate, honour trauma and transform grief into action.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.045
GPT teacher head0.374
Teacher spread0.328 · 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 designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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