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The Effectiveness of English Secondary Schools for Pupils of Different Ability Levels*

2011· article· en· W2123364577 on OpenAlexaboutno aff
Lorraine Dearden, John Micklewright, Anna Vignoles

Bibliographic record

VenueFiscal Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsLeague tableQuarter (Canadian coin)Differential (mechanical device)Value (mathematics)PsychologyLeagueMathematics educationDemographic economicsEconomicsStatisticsMathematicsGeography

Abstract

fetched live from OpenAlex

Abstract ‘League table’ information on school effectiveness in England generally relies either on a comparison of the average outcomes of pupils by school (for example, mean exam scores) or on estimates of the average value added by each school. These approaches assume that the information parents and policymakers need most to judge school effectiveness is the average achievement level or gain in a particular school. Yet schools can be differentially effective for children with differing levels of prior attainment. We present evidence on the extent of differential effectiveness in English secondary schools and find that even the most conservative estimate suggests that around one‐quarter of schools in England are differentially effective for students of differing prior ability levels. This affects an even larger proportion of children, as larger schools are more likely to be differentially effective.

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)0.016
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.083
GPT teacher head0.342
Teacher spread0.259 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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