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Record W4311455289 · doi:10.55146/ajie.v51i2.50

Supporting Indigenous children’s oral storytelling using a culturally referenced, developmentally based program

2022· article· en· W4311455289 on OpenAlexaffabout
Meadow Schroeder, Erin Tourigny, Stan Bird, Jackie Ottmann, Joan Jeary, Duane Mark, Clarice Kootenay, Susan A. Graham, Anne McKeough

Bibliographic record

VenueThe Australian Journal of Indigenous Education · 2022
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsFirst Nations University of CanadaUniversity of Calgary
Fundersnot available
KeywordsStorytellingIndigenousCurriculumLiteracyIndigenous cultureTraditional knowledgePsychologyNarrativePedagogyMedical educationMedicine

Abstract

fetched live from OpenAlex

Indigenous communities in Canada have struggled with systemic inequities that have affected education outcomes of their children. In collaboration with a Stoney Nakoda community in Western Canada, a university research team, composed of Indigenous and non-Indigenous members, offered an instruction program designed to use storytelling as a gateway to early literacy development. Indigenous researchers and collaborators guided program adaptation to increase its cultural relevance, and non-Indigenous researchers drew upon developmental research to tailor scaffolded instruction that supported increased story-structure complexity. A total of 100 children aged 5 to 7 years participated in an eight-month storytelling program, which included pre- and post-instruction assessments of storytelling and recall. After instruction, participants generated more complex, detailed stories that contained more references to their culture compared to same-age peers. They also more accurately recalled the gist of stories they were read. This study demonstrates the importance of making curricula relevant to Indigenous children by including content that is culturally relevant and developmentally appropriate.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.370
Teacher spread0.330 · 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 designObservational
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

Citations6
Published2022
Admission routes2
Has abstractyes

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