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Record W4387938230 · doi:10.1177/1476718x231188466

Guidelines for virtual early childhood and family learning: An equity, diversity, inclusion, and decolonization-informed systematic review of the literature

2023· article· en· W4387938230 on OpenAlexaff
Rachel Heydon, Elizabeth Akiwenzie, Emma Cooper, Hanaa Ghannoum, Danielle Havord-Wier, Bronwyn Johns, Kelly-Ann MacAlpine, Lori McKee, Joelle Nagle, Erica Neeganagwedgin, Danica Pawlick Potts, Sandra Poczobut, Carla Ruthes Coelho, Anna Stooke, Annie Tran, Zheng Zhang

Bibliographic record

VenueJournal of Early Childhood Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of TorontoSt. Francis Xavier UniversityUniversity of SaskatchewanWestern University
Fundersnot available
KeywordsNegotiationInclusion (mineral)Equity (law)Early childhood educationKnowledge managementDiversity (politics)SociologyPsychologyPublic relationsPedagogyEngineering ethicsComputer sciencePolitical scienceSocial psychologySocial scienceEngineering

Abstract

fetched live from OpenAlex

This article presents an equity-informed systematic review of research pertinent to the offering of virtual early childhood education programming to young children and their families. Findings are presented as guidelines which may shape the delivery of future programming within virtual contexts. These findings are organized within three major areas that were identified through the methodology: Building Connections and Fostering Online Relationships; Interactive Virtual Programming, Digital Tools, and Responsiveness; and Digital Technologies, Considerations for Access, Use, Professional Learning, and Safety. Findings highlight that developing inclusive, meaningful, and collaborative programs within virtual spaces is necessary for maximizing the learning opportunities and engagement of all children and families. Developing such services requires the careful negotiation and consideration of a range of worldviews, knowledges, priorities, and interests within unique families and contexts. Practice implications are drawn from the research, opportunities for pedagogical change are identified, and future research needs are provided.

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)0.006
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.004
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 head0.092
GPT teacher head0.417
Teacher spread0.325 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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