Children's learning and development in conflict- and crisis-affected countries: Building a science for action
Bibliographic record
Abstract
This paper critically reviews the opportunities and challenges in designing and conducting actionable research on the learning and development of children in conflict- and crisis-affected countries. We approached our review through two perspectives championed by Edward Zigler: (a) child development and social policy and (b) developmental psychopathology in context. The aim of the work was to answer the following questions: What works to enhance children's learning and development in such contexts? By what mechanisms? For whom? Under what conditions? How do experiences and conditions of crisis affect the basic processes of children's typical development? The review is based on a research-practice partnership started in the Democratic Republic of the Congo in 2010 and expanded to research in Niger and Lebanon in 2016. The focus of the research is on the impact of Healing Classrooms (a set of classroom practices) and Healing Classrooms Plus (an additional set of targeted social and emotional learning activities), developed by the International Rescue Committee, on children's academic outcomes and social and emotional learning. We sought to extract lessons from this decade of research for building a global developmental science for action. Special attention is paid to the importance of research-practice partnerships, conceptual frameworks, measurement and methodology. We conclude by highlighting several essential features of a global developmental science for action.
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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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".