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Motivational Effects and Public Funding for Special Education

2022· book-chapter· en· W4416746612 on OpenAlexaff
Tammy Kolbe, Elizabeth Dhuey

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpecial educationPublic fundingAffect (linguistics)Education policyEducation ActPublic educationState (computer science)Public policy

Abstract

fetched live from OpenAlex

Abstract In this chapter, we explore how education funding systems, inclusive of funding programs for general and special education, may affect whether and how students with disabilities receive special education and related services. And yet, funding should not affect a child with a disability’s opportunity to access public schooling or their learning. Federal law obligates states and local education agencies to provide—at no cost to students and parents—special education and related services to children with disabilities that are appropriate to their needs. Given the chasm between policy and practice, it is both necessary and important to understand how state policies for funding schools impact local decision making. We review the legal and regulatory frameworks that establish the requirement for public funding for special education and the existing research on motivational effects of programs designed to provide specific funding for special education. We then present new evidence about the connection between general education funding and special education identification rates. The chapter concludes with considerations for policymakers as they seek to develop and evaluate education finance systems that ensure students with disabilities equitable access to educational opportunities.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.900
Threshold uncertainty score0.994

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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