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Record W3087218900 · doi:10.1017/s0954579420000681

Pathways to a more peaceful and sustainable world: The transformative power of children in families

2020· article· en· W3087218900 on OpenAlexfundno aff
Pia Rebello Britto, Suna Hanöz-Penney, Liliana Angelica Ponguta, Dıane Sunar, Ghassan Issa, Sascha Hein, Maria Conceição do Rosário, Maha Almuneef, Irem Korucu, Yaya Togo, Jamshed Kurbonov, Nurlan Choibekov, Hien Thi Thu Phan, Naomi Fallon, Bekir B. Artukoğlu, Franz J. Hartl, Rima Salah, Siobhán Fitzpatrick, Paul Connolly, Laura Dunne, Sarah Miller, Kyle D. Pruett, James F. Leckman

Bibliographic record

VenueDevelopment and Psychopathology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersUBS Optimus FoundationJacobs FoundationBill and Melinda Gates FoundationQueen's UniversityQueen's University BelfastUNICEFNational Institute for Health and Care Research
KeywordsGlobeTransformative learningPower (physics)Childhood developmentWork (physics)PsychologyEarly childhoodQuality (philosophy)Sustainable developmentEconomic growthPublic relationsPolitical scienceDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

This article provides an overview of selected ongoing international efforts that have been inspired by Edward Zigler's vision to improve programs and policies for young children and families in the United States. The efforts presented are in close alignment with three strategies articulated by Edward Zigler: (a) conduct research that will inform policy advocacy; (b) design, implement, and revise quality early childhood development (ECD) programs; and (c) invest in building the next generation of scholars and advocates in child development. The intergenerational legacy left by Edward Zigler has had an impact on young children not only in the United States, but also across the globe. More needs to be done. We need to work together with a full commitment to ensure the optimal development of each child.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.277
Teacher spread0.262 · 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 designQualitative
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

Citations13
Published2020
Admission routes1
Has abstractyes

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