Measurement for Change: Reflections from innovators' experiences with monitoring, evaluation, and learning systems for Early Childhood Development
Bibliographic record
Abstract
In this review paper, we explore how on-the-ground Early Childhood Development (ECD) innovators are using monitoring, evaluation, and learning (MEL) systems to guide the design and implementation of ECD programs, as well as how MEL systems can influence policy and support the achievement of impact at scale. We reflect on articles in theFrontiersseries “Effective delivery of integrated interventions in early childhood: innovations in evidence use, monitoring, evaluation, and learning.” The 31 contributions to the series reflect the breadth and depth of complexity that characterizes ECD, including global geographic spread, with studies from Asia, Europe, Africa, and Latin America and the Caribbean. Our synthesis finds that integrating MEL processes and systems into the fabric of a program or policy initiative can broaden the underlying value proposition. Specifically, ECD organizations sought to design their MEL systems to ensure programs fit the values, goals, experiences and conceptual frameworks of diverse stakeholders, so that participating makes sense to all. For example, formative, exploratory research identified the priorities and needs of the target population and frontline service providers, and informed the content and delivery of an intervention. ECD organizations also designed their MEL systems to support a shift of accountability toward broader ownership: They included delivery agents and program participants alike assubjectsrather thanobjects, through active participation in data collection, and by providing opportunities for equitable discussion of results and decision-making. Programs collected data to respond to specialized characteristics, priorities and needs, embedding program activities into existing day-to-day routines. Further, papers pointed to the importance of intentionally involving a variety of stakeholders in national and international dialogues to ensure that diverse ECD data collection efforts are aligned and multiple perspectives are considered in the development of national ECD policies. And, several papers illustrate the value of creative methods and measurement tools to integrate MEL into a program or policy initiative. Finally, our synthesis concludes that these findings align with the five aspirations that were formulated as part of theMeasurement for Changedialogue, which motivated the launch of the series.
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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 | 0.331 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.074 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".