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Record W4385442123 · doi:10.1111/jep.13914

Contrasting epistemologies: Biomedicine, narrative medicine and indigenous story medicine

2023· article· en· W4385442123 on OpenAlexafffund
Shane Neilson

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcMaster UniversityRegional Municipality of Waterloo
FundersSocial Sciences and Humanities Research Council
KeywordsNarrativeIndigenousNarrative medicineBiomedicineNarrative inquiryMythologyHistorySociologyLiteratureArtBioinformaticsClassicsBiologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Narrative Medicine (NM) and Indigenous Story Medicine both use narrative to understand and effect health, but their respective conceptualizations of narrative differ. AIMS: I contrast the concept of narrative in NM with that of Indigenous Story Medicine. MATERIALS AND METHODS: The article relies Western narrative theorists as well as Indigenous epistemologists to frame a discussion-by-contrast of the Judeo-Christian creation myth with a Haundenosaunee Creation Story. RESULTS: I demonstrate that the deficiencies of Narrative Medicine exist because the latter's use of narrative is a mere application in an otherwise reductive field, whereas Indigenous epistemologies rely on story as medicine itself. DISCUSSION: OMIT. CONCLUSION: I call for more scholars to take up different narratives to further investigate the ethical space between NM and Indigenous Story Medicine.

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)0.017
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.063
Scholarly communication0.0100.011
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.237
GPT teacher head0.561
Teacher spread0.324 · 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 designTheoretical or conceptual
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

Citations5
Published2023
Admission routes2
Has abstractyes

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