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Record W2138855767 · doi:10.1162/edfp_a_00031

Funding Special Education by Capitation: Evidence from State Finance Reforms

2011· article· en· W2138855767 on OpenAlex
Elizabeth Dhuey, Stephen Lipscomb

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducation Finance and Policy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapitationIncentiveFalling (accident)State (computer science)Differential (mechanical device)Actuarial scienceSpecial educationDemographic economicsEconomicsBusinessFinanceMedicinePsychologyPaymentMathematics education

Abstract

fetched live from OpenAlex

This study examines responses to state capitation policies for special education finance between 1991–92 and 2003–4. Capitation refers to distributing funds based on the entire student enrollment. We find that disability rates tended to fall following capitation reforms, primarily in subjectively diagnosed categories and in early and late grades. The association appears immediately in less severe categories but gradually in severe categories. More frequent program exiting partly accounts for falling disability rates among high school students. Capitation also is associated with a rising local share and a falling state share of funding. The evidence supports an increased use of outside school placements among severe disabilities, consistent with an incentive-based response. We find weaker evidence of a relationship between capitation and higher request rates for dispute resolution. Finally, we present evidence of differential effects based on both the pre-reform funding system and the presumed strength of the policy change.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.365
Teacher spread0.309 · 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