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Record W4405074279 · doi:10.1177/14639491241293474

Co-developing culturally grounded early years programming with Indigenous communities

2024· article· en· W4405074279 on OpenAlexafffundabout
Mélissa Tremblay, Janoah Willsie, Heather Downie, Charlene Rattlesnake, Bryan Kolb, Rebecca Gokiert, Jessica Hayden, Barbara Fallon

Bibliographic record

VenueContemporary Issues in Early Childhood · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of TorontoUniversity of LethbridgeUniversity of Alberta
FundersFondation Brain Canada
KeywordsIndigenousEarly childhoodEarly childhood educationGrounded theoryRelevance (law)Economic growthTraditional knowledgeProgram Design LanguageSociologyPolitical sciencePublic relationsPedagogyPsychologyDevelopmental psychologyQualitative researchSocial scienceEngineering

Abstract

fetched live from OpenAlex

Although early childhood programs designed for Indigenous children exist in Canada and elsewhere, there remains a need for program development and implementation to be sustainable, evidence-based, and foundationally grounded in (rather than peripherally inclusive of) Indigenous cultures and languages. Overall, little is known about how to develop sustainable, communitygrounded early childhood programs that address the structural inequities and health disparities that disproportionately impact Indigenous children and families. The purpose of this paper is to describe the development of a community-based prenatal to preschool initiative that centralizes Indigenous knowledge and cultural values. Through this paper, we describe the process of codeveloping the Early Years (EY) program in Maskwacis, Alberta. We provide a description of program partners, summarize existing programs used to shape the EY program model, and outline the program model. Finally, we discuss relevance to other communitybased initiatives across sectors, and learnings that are transferable to other community contexts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.313
Teacher spread0.285 · 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
Published2024
Admission routes3
Has abstractyes

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