Evaluation in the field of early childhood development: A scoping review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".