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Record W994140994 · doi:10.26522/tl.v5i1.298

Ready or Not, Here They Come: Inclusion of Invisible Disabilities in Post-Secondary Education

2009· article· en· W994140994 on OpenAlexaffvenueabout
Karen Csoli, Sheila Bennett, Tiffany L. Gallagher

Bibliographic record

VenueTeaching and Learning · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsBrock University
Fundersnot available
KeywordsInclusion (mineral)Equity (law)LegislationSpecial educationUniversal Design for LearningUniversal designSecondary educationPostsecondary educationPedagogyMathematics educationPsychologyLearning disabilityMedical educationPolitical scienceHigher educationMedicineSocial psychologyDevelopmental psychologyEngineeringLaw

Abstract

fetched live from OpenAlex

In Ontario, elementary and secondary school programs such as "School Success" and legislation such as Education for All have greatly increased the success of students with disabilities. Success at the secondary school level means that more students with disabilities are choosing to attend postsecondary institutions. This paper focuses on the transition of students with invisible disabilities from secondary to post-secondary education. Universal Instructional Design is reviewed as an appropriate teaching tool for the postsecondary level, as it allows for increased access to meaningful learning experiences for students with and without disabilities. At this point in time, rights-based inclusion is still a novel concept and post-secondary educators struggle with what it means and what it looks like to include learners with disabilities. Issues that prevent the rights of individuals with special needs from being realized include access to higher education, limited funding, and employment equity.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.009
Scholarly communication0.0060.004
Open science0.0020.014
Research integrity0.0020.003
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 head0.030
GPT teacher head0.359
Teacher spread0.329 · 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
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

Citations2
Published2009
Admission routes3
Has abstractyes

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