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Record W2735685672 · doi:10.20360/g2ct05

Children’s Funds of Knowledge in a Rural Northern Canadian Community: A Telling Case

2017· article· en· W2735685672 on OpenAlexaffvenueabout
Jim Anderson, Laura Horton, Maureen Kendrick, Marianne McTavish

Bibliographic record

VenueLanguage and Literacy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of British Columbia
FundersUniversity of CambridgeHarvard University
KeywordsTraditional knowledgeIndigenousCultural knowledgeCultural assimilationEarly childhoodPedagogySociologyKnowledge sharingEarly childhood educationPolitical sciencePublic relationsPsychologyAnthropologyDevelopmental psychologyManagementEthnic group

Abstract

fetched live from OpenAlex

In this article, we describe how the funds of knowledge in a community in rural Northern Canada were actualized or leveraged in an early childhood classroom. We draw on a video recording of a First Nations elder demonstrating to the children (and early childhood educators) how to skin a marten, a historical cultural practice of the community. We argue that elders are an untapped source of knowledge that preschools and schools can call on to legitimize and bring to the forefront, Indigenous knowledge that has been ignored or undervalued by assimilationist and colonialist policies. We also argue that the elder’s demonstration is culturally congruent with First Nations traditions of sharing or passing on knowledge and that it is imperative that educators are aware of and implement culturally appropriate pedagogical practices. We conclude by sharing some ideas of how early childhood educators might facilitate through play, children’s taking up and appropriating cultural knowledge such as the elder shared in this case.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.053
Threshold uncertainty score0.388

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0520.017
Scholarly communication0.0050.002
Open science0.0020.006
Research integrity0.0030.005
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 head0.323
Teacher spread0.308 · 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

Citations9
Published2017
Admission routes3
Has abstractyes

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