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Record W2318228741 · doi:10.1177/1468798411417378

‘They tell a story and there's meaning behind that story’: Indigenous knowledge and young indigenous children's literacy learning

2011· article· en· W2318228741 on OpenAlexaffabout
J. Laurence Hare

Bibliographic record

VenueJournal of Early Childhood Literacy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousLiteracyTraditional knowledgeMeaning (existential)SociologyPedagogyEarly childhoodIndigenous educationEarly childhood educationGender studiesPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

This research draws on the reflections from group discussions with indigenous families and interviews with early childhood educators and community stakeholders from five First Nations reserve communities in Canada whose young children participate in the national aboriginal Head Start On Reserve (AHSOR) programme. The purpose of the study was to examine the contributions of indigenous knowledge to young indigenous children's literacy learning. In the course of this examination what became clear is that there is a greater set of literacy activities in these families than is recognized by early learning settings. Further, there is a literacy orientation within their indigenous knowledge systems that, draws on oral tradition, land-based experiences and ceremonial practices that, when linked to the discourses of schooling and literacy, provide the basis for improving educational outcomes for indigenous children and families, whose relationship with schooling has been historically troubled.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.176
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.015
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations76
Published2011
Admission routes2
Has abstractyes

Explore more

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