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Record W4413908305 · doi:10.1075/sin.28

Storytelling, Identity Formation, and Resistance in Indigenous Cultures in Canada and the United States

2025· book· en· W4413908305 on OpenAlexaboutno aff

Bibliographic record

VenueStudies in narrative · 2025
Typebook
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingIndigenousResistance (ecology)Identity (music)GenealogyGender studiesPolitical scienceEthnologyAnthropologySociologyHistoryNarrativeArtLiteratureAestheticsBiology

Abstract

fetched live from OpenAlex

Storytelling is a means of fostering a sense of identity, belonging, and continuity. Through stories, Indigenous peoples understand and interpret the world, and learn how to survive in spite of external forces such as colonialism. Storytelling has been studied by many scholars across myriad disciplines; however, its importance in dealing with trauma and in shaping identity demand further study. This volume contributes to an understanding of the importance of storytelling in shaping identity and healing trauma, and as a method of resistance among Indigenous peoples in North America. The book will attract readers interested in Native North American studies, Canadian studies, and cultural studies. In particular, the audience will include scholars investigating the importance of storytelling and its impact on healing and resistance among Indigenous peoples in Canada and the United States. The contributions in this volume cover a wide range of media: fiction and non-fiction works, documentaries, poetry, activist work, movies, and TV series.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.054
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0160.007
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.015
GPT teacher head0.319
Teacher spread0.304 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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