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Record W3121953391

The Effects of School Board Consolidation and Financing on Student Performance

2010· preprint· en· W3121953391 on OpenAlexfundaboutno aff
John Leach, A. Abigail Payne, Steve Chan

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaOntario Innovation Trust
KeywordsConsolidation (business)Student achievementPolitical sciencePublic administrationBusinessPublic relationsFinanceAcademic achievementMathematics educationPsychology
DOInot available

Abstract

fetched live from OpenAlex

Over the last 20 years, states and provinces have become increasingly involved in the financing and administration of elementary and secondary education. Local school boards, however, still retain control over key aspects of the provision of education. Historically, these boards were organized at the community level so as to meet the wants of the local community. Today, states and provinces have become more interested in consolidating school boards and moving to a more centralized funding scheme. Do these changes result in improved student achievement? This paper attempts to answer these questions by examining the school board consolidation and funding changes instituted by the province of Ontario. We differentiate the effects of the policy changes based on observed differences in the school boards prior to consolidation. We show that students in previously high wealth school boards perform worse after the policy change compared to students in previously low wealth school boards.

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)0.002
metaresearch head score (Gemma)0.015
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 score0.180
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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