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Record W2146457157 · doi:10.1111/ecoj.12267

Does Management Matter in schools?

2015· article· en· W2146457157 on OpenAlexaboutno aff
Nicholas Bloom, Renata Lemos, Raffaella Sadun, John Van Reenen

Bibliographic record

VenueThe Economic Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsLibrary scienceSociologyMedia studiesComputer science

Abstract

fetched live from OpenAlex

We collect data on management practices in over 1,800 high schools in eight countries. We show that higher management quality is strongly associated with better educational outcomes. The UK, Sweden, Canada and the US obtain the highest management scores, followed by Germany, with a gap before Italy, Brazil and India. We also show that autonomous government schools (government funded but with substantial independence like UK academies and US charters) have higher management scores than regular government or private schools. Almost half of the difference between the management scores of autonomous and regular government schools is accounted for principal leadership and governance.

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)0.005
metaresearch head score (Gemma)0.024
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.299
Teacher spread0.272 · 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

Citations289
Published2015
Admission routes1
Has abstractyes

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