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Inclusive Postsecondary Education for People with Intellectual Disabilities: Teaching Methodologies and Practices of Translating Rights into Practice

2024· article· en· W4399890979 on OpenAlexaboutno aff
Nicola Maxwell, Elaine O’Callaghan, Máire Leane

Bibliographic record

VenuePedagogia i Treball Social · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsConvention on the Rights of Persons with DisabilitiesInclusion (mineral)DocumentationIntellectual disabilityPedagogyCornerstonePolitical scienceSpecial educationDisability studiesIrishSociologyConventionMedical educationPublic relationsPsychologyMedicineLawSocial science

Abstract

fetched live from OpenAlex

Article 24 of the Convention on the Rights of Persons with Disabilities (CRPD) forms the cornerstone of the right to “inclusive education”, outlining in subsection 1 that, States “shall ensure an inclusive education system at all levels” (2006). The Committee on the Rights of Persons with Disabilities has stated that inclusion “involves a process of systemic reform embodying changes and modifications in content, teaching methods, approaches, structures and strategies in education” (2016, [11]). However, this right to inclusive education has not been realised in any systematic way for students with intellectual disabilities (ID) in Postsecondary education (PSE). This article will (1) explore the right to inclusive education for people with disability established under the CRPD (2) review existing research on pedagogies and practices for teaching students with ID in PSE models in Ireland, Canada, the US and Australia, (3) describe how a rights-based PSE programme was developed in an Irish university (UCC), and (4) argue that critical reflection, documentation and sharing of pedagogies and practices of rights-based PSE is key to development and dissemination of practice insights in this emergent field of education.

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)0.002
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.093
GPT teacher head0.491
Teacher spread0.398 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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