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Record W2982644662 · doi:10.18357/jcs444201919209

Colonialism in Early Education, Care, and Intervention: A Knowledge Synthesis

2019· article· en· W2982644662 on OpenAlexafffundvenue
Kathryn Underwood, Nicole Ineese-Nash, Arlene Haché

Bibliographic record

VenueJournal of Childhood Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousGeneral partnershipEarly childhood interventionEarly childhood educationEarly childhoodIntervention (counseling)Context (archaeology)Traditional knowledgeNursingSociologyPedagogyMedical educationPolitical sciencePsychologyMedicineDevelopmental psychologyGeography

Abstract

fetched live from OpenAlex

This knowledge synthesis aims to understand Indigenous experiences of early childhood education, care, family support, intervention, health, and Indigenous services in the context of childhood disability. Each of these institutional contexts has its own underlying professional discourses and worldviews. Knowledge from three sources have been synthesized: (1) interviews with Indigenous families about their experiences of having disabled children, conducted through the Inclusive Early Childhood Service System (IECSS) Project; (2) analysis of the IECSS interviews by the Districtof Temiskaming Elders Council and Indigenous community partners; and (3) the existing body of literature on disability and Indigenous children.This project was conducted in partnership with a mixed team of Indigenous and settler researchers.

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)0.016
metaresearch head score (Gemma)0.023
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: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.008
Science and technology studies0.0040.005
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.333
Teacher spread0.322 · 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
GenreReview

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

Citations26
Published2019
Admission routes3
Has abstractyes

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