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Record W2800118687 · doi:10.1080/15595692.2018.1462156

Teaching Life Narratives in the Classroom: Strategies Based on Indigenous Traditions

2018· article· en· W2800118687 on OpenAlexafffundabout
Constance Lavoie, Patricia-Anne Blanchet

Bibliographic record

VenueDiaspora Indigenous and Minority Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCommission Scolaire des Hautes RivièresUniversité du Québec à Chicoutimi
FundersFonds de Recherche du Québec-Société et CultureUniversité de Sherbrooke
KeywordsIndigenousNarrativeMohawkSociologyIndigenous educationTraditional knowledgePedagogyNarrative inquiryLibrary scienceComputer scienceArtLiteratureEcology

Abstract

fetched live from OpenAlex

The life narrative is an oral genre grounded in Indigenous tradition and teaching practice. In Canadian Indigenous communities, the expertise and content transmitted by life narratives are a part of their oral heritage. Drawing from their personal and professional experiences in Indigenous school environments, as well as the results from exploratory studies, the authors have developed an educational model for teaching life narratives in the classroom. This paper begins with an overview on the pedagogical use of life narratives for historical reconciliation, knowledge and expertise preservation, and ethical education. This enables the authors to propose a definition supported by the existing scientific literature. An analysis of the data collected from Innu, Algonquin, and Mohawk communities revealed the main strategies commonly used by their teachers and Elders. A pedagogical model dividing these strategies into planning, integration, and implementation phases for using life narratives is finally presented.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.315
Teacher spread0.293 · 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 teacher head, not a consensus.

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

Citations2
Published2018
Admission routes3
Has abstractyes

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