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Record W2131821682 · doi:10.1080/01411920903083111

Is children's free school meal ‘eligibility’ a good proxy for family income?

2009· article· en· W2131821682 on OpenAlexaboutno aff
Graham Hobbs, Anna Vignoles

Bibliographic record

VenueBritish Educational Research Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsReceiptProxy (statistics)Family incomeQuarter (Canadian coin)Earned income tax creditDemographic economicsSocioeconomic statusPaymentEconomicsPovertyEconomic growthDemographySociologyGeographyFinance

Abstract

fetched live from OpenAlex

Family income is an important factor associated with children's educational achievement. However, key areas of UK research (for example, on socially segregated schooling) and policy (for example, the allocation of funding to schools) rely on children's free school meal (FSM) ‘eligibility’ to proxy family income. This article examines the relationship between children's FSM ‘eligibility’ and equivalent net household income in a nationally representative survey of England (the Family Resources Survey). It finds that children ‘eligible’ for FSM are much more likely than other children to be in the lowest income households. However, only around one‐quarter to one‐half of them were in the lowest income households in 2004/5. This is principally because the receipt of means‐tested benefits (and tax credits) pushes children eligible for FSM up the household income distribution. The implications for key areas of research and policy are discussed.

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)0.004
metaresearch head score (Gemma)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.096
GPT teacher head0.485
Teacher spread0.389 · 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

Citations177
Published2009
Admission routes1
Has abstractyes

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