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Record W2899017378 · doi:10.1086/718328

Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children

2022· article· en· W2899017378 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Labor Economics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsDisadvantagedClass sizeCognitive developmentClass (philosophy)CognitionCensusSocial classPsychologyScale (ratio)Nonlinear systemDemographic economicsEconomicsMathematics educationEconomic growthDemographySociologyComputer scienceGeography

Abstract

fetched live from OpenAlex

We estimate the nonlinear impact of class size on student achievement by exploiting regulations that cap class size at 20 students per class in kindergarten. Based on student-level information from a previously unexploited and unique large-scale census survey of kindergarten students, this study provides clear evidence of the nonlinearity of class size effects on development measures. While the effects are largest on cognitive development, class size reductions also improve noncognitive skills for children living in disadvantaged areas. These findings suggest that sizeable class size reductions targeted at disadvantaged areas would achieve better results than a marginal reduction across the board.

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.

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 score0.091
Threshold uncertainty score0.296

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 studies0.0000.000
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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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