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Record W2058419219 · doi:10.1525/irqr.2013.6.4.559

Indigenous Storytelling as Research

2013· article· en· W2058419219 on OpenAlexaboutno aff
Judy M. Iseke

Bibliographic record

VenueInternational Review of Qualitative Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingIndigenousSpiritualityTraditional knowledgeSociologyPsychologyNarrativeLiteratureArtMedicineEcology

Abstract

fetched live from OpenAlex

Story is a practice in Indigenous cultures that sustains communities, validates experiences and epistemologies, expresses experiences of Indigenous peoples, and nurtures relationships and the sharing of knowledge. Storytelling is also a central focus of Indigenous epistemologies, pedagogies, and research approaches. Excerpts from discussions by Métis Elders, whose stories and histories are shared, suggest a complex mindfulness and require “deep respect” in research (Iseke & Brennus, 2011, p. 247). Elders’ stories inform discussions of (a) storytelling types (mythical, personal, and sacred), (b) storytelling as pedagogical tools for learning about life, (c) storytelling as witnessing and remembering, and (d) sharing stories of spirituality as sources of strength. Discussions follow.

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)0.022
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0080.027
Scholarly communication0.0080.009
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.382
GPT teacher head0.648
Teacher spread0.266 · 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.

Study designQualitative
Domainnot available
GenreMethods

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

Citations216
Published2013
Admission routes1
Has abstractyes

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