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Record W3042333502 · doi:10.18357/tar111202019324

The Healing Power of Storytelling: Finding Identity Through Narrative

2020· article· en· W3042333502 on OpenAlexaffvenue
Seren Micheal Friskie

Bibliographic record

VenueThe Arbutus Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsDouglas College
Fundersnot available
KeywordsStorytellingIndigenousNarrativeDanceIdentity (music)Context (archaeology)SociologyTraditional knowledgePower (physics)Gender studiesAnthropologyHistoryAestheticsVisual artsArtLiteratureEcologyArchaeology

Abstract

fetched live from OpenAlex

This paper describes the power of storytelling in the context of an Indigenous youth collective, whichgathers each week to share their lived experiences and learn song, dance, and lessons through story. Ibegin with my own life narrative followed by an exploration of how the intergenerational transmissionof historical trauma has left many Indigenous youth searching for a connection to their culture. I thendiscuss research that reveals the importance of cultural continuity, self-determination, and engagementin the community to the healing journey of Indigenous youth. Next, I consider oral storytelling as onemethod of knowledge delivery, utilized by Indigenous Nations for thousands of years, that seamlesslyblends cultural learning and thus connection to identity. I detail the creation of a Youth StorytellingCircle which centres teachings from the Stó:lō, Haida, Nisga’a, Salish, and Popkum Coast Salish Nationssurrounding the shores and rainforests of what is now British Columbia. I conclude with reasons whyengaging youth in their wellbeing through traditional practices is of high importance to us all as Indigenouscommunity members.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.006
Scholarly communication0.0060.007
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.384
Teacher spread0.314 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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