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Record W2984657878 · doi:10.32799/ijih.v14i2.31059

Living stories through a sweet grass porcupine quill box methodology:

2019· article· en· W2984657878 on OpenAlexvenueno aff
Mary Smith

Bibliographic record

VenueInternational Journal of Indigenous Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStorytellingFeelingSociologyAutoethnographyPsychologyAestheticsGender studiesSocial psychologyNarrativeArtEcologyBiologyLiterature

Abstract

fetched live from OpenAlex

Abstract The twofold purposes of this research project were to engage a distinctive Indigenous methodology and gather the living stories regarding community health experiences pertaining to Chronic Kidney Disease. An artistically inspired Indigenous methodology arose within contextual ways of being and knowing within the community, expressed through the art and crafting of a sweetgrass and porcupine quill box. The methods respected traditional community protocols prioritizing sharing circles involving elders and storytelling. The living stories from ten participants illuminated relationality and cultural knowledge as a strength amid fears and feelings of mistrust. Additionally, inequitable access and racialized health care also emerged as root factors leading to decreased participation in health care. The participant’s sharing of dreams revealed wisdom and interpretations that created living stories where dreams are enmeshed in all daily moments to guide and direct.

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)0.008
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 score0.011
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.007
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.051
GPT teacher head0.404
Teacher spread0.353 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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