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Record W2581324808 · doi:10.1017/s0021911816001650

“Anything Can Be Used to Stimulate Child Development”: Early Childhood Education and Development in Indonesia as a Durable Assemblage

2017· article· en· W2581324808 on OpenAlexfundno aff
Jan Newberry

Bibliographic record

VenueThe Journal of Asian Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersNational University of SingaporeUniversity of Lethbridge
KeywordsDemocratizationCurriculumContingencyEarly childhoodMiddle classEconomic growthAssemblage (archaeology)Contingency planPolitical scienceSociologyDevelopment economicsHistoryPedagogyPsychologyDevelopmental psychologyManagementEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

An explosion of early childhood programs in Yogyakarta, Indonesia, has followed on disaster, democratization, growth of the middle class, and global neoliberal reform at the beginning of the twenty-first century. New forms of professional expertise have emerged as a part of this global assemblage to deal with the expanded notions of development advocated by the World Bank and other intergovernmental organizations. Yet, what has remained relatively unremarked is the continued reliance on older New Order forms of social welfare linked to the community form and women's labor. Here, trauma healing programs aimed at the young after the 2006 earthquake, new preschools and playgroups in the era of democratization, and the proliferation of international curricula and pedagogy illustrate how the restless contingency associated with global assemblages is rooted in durable forms of community organization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.355
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes1
Has abstractyes

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