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Record W3016563824

Neurodiversity in Public Schools: A Critique of Special Education in America

2020· article· en· W3016563824 on OpenAlexaboutno aff
Pallavi M. Vishwanath

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsPublic educationPolitical scienceSociologyPsychologyPublic administration
DOInot available

Abstract

fetched live from OpenAlex

This Note provides a comparative critique to the special education practices in the U.S. and Canada. The Note reasons that a country or democracy is most benefitted when there is a recognized governmental duty to maximize the potential of every student via public education. The Note further exposes how a difference in governmental duty to provide equal education drastically affects students’ dignity and potential. This Note describes the history of the American public education system; explains the development of special education in the United States and the ambiguous governmental duty to educate American students; and discusses Canadian case law regarding special education to show that providing access to equal educational opportunities promotes a healthy democracy. Ultimately, this Note argues that in defining education as a right, rather than a service, America can better establish equality in public education and foster the human potential of all students to become productive members of society.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.197
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0190.048
Scholarly communication0.0090.005
Open science0.0020.004
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.280
Teacher spread0.245 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2020
Admission routes1
Has abstractyes

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