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Record W2217534465 · doi:10.21307/eb-2014-001

Class size and academic results, with a focus on children from culturally, linguistically and economically disenfranchised communities

2014· article· en· W2217534465 on OpenAlexaboutno aff
David Zyngier

Bibliographic record

VenueEvidence Base · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Focus (optics)Mathematics educationSociologyClass sizePsychologyComputer scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

The question of class size continues to attract the attention of educational policymakers and researchers alike. Australian politicians and their advisers, policy makers and political commentators agree that much of Australia’s increased expenditure on education in the last 30 years has been ‘wasted’ on efforts to reduce class sizes. They conclude that funding is therefore not the problem in Australian education, arguing that extra funding has not led to improved academic results. Many scholars have found serious methodological issues with the existing reviews that make claims for the lack of educational and economic utility in reducing class sizes in schools. Significantly, the research supporting the current policy advice to both state and federal ministers of education is highly selective, and based on limited studies originating from the USA. This comprehensive review of 112 papers from 1979-2014 assesses whether these conclusions about the effect of smaller class sizes still hold. The review draws on a wider range of studies, starting with Australian research, but also includes similar education systems such as England, Canada, New Zealand and non-English speaking countries of Europe. The review assesses the different measures of class size and how they affect the results, and also whether other variables such as teaching methods are taken into account. Findings suggest that smaller class sizes in the first four years of school can have an important and lasting impact on student achievement, especially for children from culturally, linguistically and economically disenfranchised communities. This is particularly true when smaller classes are combined with appropriate teacher pedagogies suited to reduced student numbers. Suggested policy recommendations involve targeted funding for specific lessons and schools, combined with professional development of teachers. These measures may help to address the inequality of schooling and ameliorate the damage done by poverty, violence, inadequate childcare and other factors to our children’s learning outcomes.

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)0.006
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.038
GPT teacher head0.329
Teacher spread0.292 · 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 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

Citations25
Published2014
Admission routes1
Has abstractyes

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