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Record W4389687090 · doi:10.4337/9781802204056.00025

Higher education for students with intellectual disability: expanding research, policy, and practice

2023· book-chapter· en· W4389687090 on OpenAlexaboutno aff
Meg Grigal, Clare Papay, Michelle L. Bonati

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsCredentialLegislationPolitical scienceAccreditationHigher educationEconomic growthPublic relationsPublic administration

Abstract

fetched live from OpenAlex

This chapter describes the emerging higher education options for students with intellectual disability (ID) in the United States and other countries. Access to higher education has become more available to students with ID in recent years, impacting the knowledge and practices used in secondary and higher education. In the United States, much of the program development activity has been connected to federal legislation and funding. In other countries such as Australia, Canada, Ireland, and Portugal, these initiatives are less driven by national agendas or funding but are implemented in a more localized manner by specific universities or communities. Background information on the development of these initiatives and the focal issues for program development, research, and policy for the past decade will be provided across these diverse contexts. The chapter concludes by highlighting next generation issues, such as faculty training and personnel preparation, credential development, and program accreditation.

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)0.015
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0040.010
Scholarly communication0.0140.012
Open science0.0020.010
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0140.002

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 head0.194
GPT teacher head0.468
Teacher spread0.275 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueEdward Elgar Publishing eBooksSame topicDisability Education and EmploymentFrench-language works237,207