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Record W3120084887 · doi:10.1017/s0954579420001789

Children's learning and development in conflict- and crisis-affected countries: Building a science for action

2021· article· en· W3120084887 on OpenAlexfundno aff
J. Lawrence Aber, Carly Tubbs Dolan, Ha Yeon Kim, Lindsay Brown

Bibliographic record

VenueDevelopment and Psychopathology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersYork UniversityEconomic and Social Research CouncilDepartment for International DevelopmentNew York University Abu DhabiUnited States Agency for International Development
KeywordsGeneral partnershipContext (archaeology)PsychologyAction (physics)Action researchSet (abstract data type)Political sciencePublic relationsPedagogy

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.361
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2021
Admission routes1
Has abstractyes

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Same venueDevelopment and PsychopathologySame topicEarly Childhood Education and DevelopmentFrench-language works237,207