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Record W4221079982 · doi:10.1177/1035719x221080575

Evaluation in the field of early childhood development: A scoping review

2022· review· en· W4221079982 on OpenAlexafffundabout
Giulia Puinean, Rebecca Gokiert, Mischa Taylor, Shelly Jun, Pieter de Vos

Bibliographic record

VenueEvaluation Journal of Australasia · 2022
Typereview
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBridging (networking)Field (mathematics)Evaluation methodsInclusion (mineral)Program evaluationEarly childhood educationImpact evaluationCapacity buildingEarly childhoodPsychologyPolitical scienceManagement scienceComputer scienceEngineeringMedicinePedagogySocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Children’s early experiences and environments profoundly impact their development; therefore, ensuring the well-being of children through effective supports and services is critical. Evaluation is a tool that can be used to understand the effectiveness of early childhood development (ECD) practices, programs, and policies. A deeper understanding of the evaluation landscape in the ECD field is needed at this time. The purpose of this scoping review was to explore the state of evaluation in the ECD field across four constructs: community-driven evaluation, culturally responsive evaluation, evaluation capacity building, and evaluation use and influence. A comprehensive search of 7 electronic databases, including Canadian and international literature published in English from 2000 to 2020, was conducted. A total of 30 articles met the inclusion criteria. Findings demonstrate that some studies include aspects of a community-engaged approach to evaluation; however, comprehensive approaches to community-driven evaluation, culturally responsive evaluation, evaluation capacity building, and evaluation use in the field of ECD are not commonly achieved. This review will inform strategies for bridging evaluation gaps in the ECD sector, ultimately equipping organisations with the evaluative tools to improve practices, programs, and policies that impact the children, families, and communities they serve.

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)0.142
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1420.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.000

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 head0.433
GPT teacher head0.595
Teacher spread0.162 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations9
Published2022
Admission routes3
Has abstractyes

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