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Record W2791519179 · doi:10.2196/resprot.8722

Building Yolŋu Skills, Knowledge, and Priorities into Early Childhood Assessment and Support: Protocol for a Qualitative Study

2018· article· en· W2791519179 on OpenAlexvenueno aff
Anne Lowell, Elaine Maypilama, Lyn Fasoli, Rosemary Gundjarranbuy, Jenine Godwin-Thompson, Abbey Guyula, Megan Yunupiŋu, Emily Armstrong, Jane Garrutju, Rose McEldowney

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Qualitative researchPsychologyMedical educationKnowledge managementComputer scienceMedicineSociologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Yolngu or Yolŋu are a group of indigenous Australian people inhabiting north-eastern Arnhem Land in the Northern Territory of Australia. Recent government policy addressing disparities in outcomes between Indigenous and other children in Australia has resulted in the rapid introduction of early childhood interventions in remote Aboriginal communities. This is despite minimal research into their appropriateness or effectiveness for these contexts. OBJECTIVE: This research aims to privilege Aboriginal early childhood knowledge, priorities and practices and to strengthen the evidence base for culturally responsive and relevant assessment processes and support that distinguishes "difference" from "deficit" to facilitate optimal child development. METHODS: This collaborative qualitative research employs video ethnography, participant observation and in-depth interviews, involving Aboriginal families and researchers in design, implementation, interpretation and dissemination using a locally developed, culturally responsive research approach. Longitudinal case studies are being conducted with 6 families over 5 years and emerging findings are being explored with a further 50 families and key community informants. Data from all sources are analyzed inductively using a collaborative and iterative process. The study findings, grounded in an in-depth understanding of the cultural context of the study but with relevance to policy and practice more widely, are informing the development of a Web-based educational resource and targeted knowledge exchange activities. RESULTS: This paper focuses only on the research approach used in this project. The findings will be reported in detail in future publications. In response to community concerns about lack of recognition of Aboriginal early childhood strengths, priorities and knowledge, this collaborative community-driven project strengthens the evidence base for developing culturally responsive and relevant early childhood services and assessment processes to support optimal child development. The study findings are guiding the development of a Web-based educational resource for staff working with Aboriginal communities and families in the field of early child development. This website will also function as a community-developed tool for strengthening and maintaining Aboriginal knowledge and practice related to child development and child rearing. It will be widely accessible to community members through a range of platforms (eg, mobile phones and tablets) and will provide a model for other cultural contexts. CONCLUSIONS: This project will facilitate wider recognition and reflection of cultural knowledge and practice in early childhood programs and policies and will support strengthening and maintenance of cultural knowledge. The culturally responsive and highly collaborative approach to community-based research on which this project is based will also inform future research through sharing knowledge about the research process as well as research findings.

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)0.085
metaresearch head score (Gemma)0.050
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.085
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.050
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.004
Science and technology studies0.0090.006
Scholarly communication0.0050.005
Open science0.0060.005
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0640.013

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 head0.170
GPT teacher head0.641
Teacher spread0.470 · 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
GenreProtocol

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

Citations15
Published2018
Admission routes1
Has abstractyes

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