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Record W2525284938 · doi:10.1177/1463949116661126

The Organisation for Economic Co-operation and Development’s International Early Learning Study: Opening for debate and contestation

2016· article· en· W2525284938 on OpenAlexaff
Peter Moss, Gunilla Dahlberg, Susan Grieshaber, S Mantovani, Helen May, Alan Pence, Sylvie Rayna, Beth Blue Swadener, Michel Vandenbroeck

Bibliographic record

VenueContemporary Issues in Early Childhood · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsUniversity of Victoria
FundersStockholms UniversitetMonash University
KeywordsEarly childhoodDemocracyPolitical scienceEarly childhood educationEconomic growthProcess (computing)SociologyPedagogyPublic relationsPsychologyEconomicsDevelopmental psychologyPoliticsLaw

Abstract

fetched live from OpenAlex

The Organisation for Economic Co-operation and Development is initiating the International Early Learning Study, a cross-national assessment of early learning outcomes involving the testing of 5-year-old children in participating countries. The authors use this colloquium to inform members of the early childhood community about this project and to raise concerns about its assumptions, practices and possible effects. The authors also invite readers’ comments, to start a process of democratic dialogue and contestation.

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)0.045
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.009
Science and technology studies0.0040.010
Scholarly communication0.0150.012
Open science0.0020.009
Research integrity0.0120.013
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.024
GPT teacher head0.330
Teacher spread0.306 · 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.

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

Citations160
Published2016
Admission routes1
Has abstractyes

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