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Exploring the Intersections of Storytelling and Visual Arts: Indigenous Peoples’ Experiences of Cancer

2019· article· en· W2948706554 on OpenAlexaboutno aff
E. J. Thomas, Novy, Gifford, Grandpierre, Poudrier, Thomas Thomas

Bibliographic record

VenueStorytelling Self Society · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStorytellingContext (archaeology)The artsJournaling file systemQualitative researchTraditional knowledgeVisual methodsSociologyVisual artsNarrativePsychologySocial scienceGeographyArtEcologyArchaeology

Abstract

fetched live from OpenAlex

Known gaps in health and social care, largely stemming from colonization, result in poorer outcomes for Indigenous peoples with cancer as compared to non-Indigenous peoples. Also, few researchers have focused on the strengths of Indigenous peoples in dealing with such challenges. Of note is a lack of research exploring Indigenous knowledge in this context and the ways in which such knowledge may be conveyed through stories and visual arts. With a view to exploring Indigenous cancer experiences, we completed a qualitative project with five communities in Canada. Data were collected via sharing sessions, photography and journaling, and individual interviews; all of these methods resulted in stories that were selected and shared by the participants themselves in their own words. The intersections of storytelling and visual arts were interpreted, resulting in three themes:

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)0.007
metaresearch head score (Gemma)0.008
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 score0.144
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0170.023
Scholarly communication0.0070.004
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.070
GPT teacher head0.368
Teacher spread0.298 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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