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Record W2141877629 · doi:10.26522/tl.v4i3.266

Understanding Disability and Culture while Enhancing Advocacy

2008· article· en· W2141877629 on OpenAlexaffvenueabout
Tiffany L. Gallagher, Carla DiGiorgio, Sheila Bennett, Karen Antle

Bibliographic record

VenueTeaching and Learning · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsCollege of the North AtlanticUniversity of Prince Edward IslandBrock University
Fundersnot available
KeywordsInternshipMulticulturalismDisability studiesPerspective (graphical)PedagogyHuman rightsPolitical scienceSociologyPublic relationsMedical educationPsychologyMedicineLaw

Abstract

fetched live from OpenAlex

In Canada’s increasingly multicultural society, a common understanding of the basic rights of individuals with disabilities in educational and workplace settings is essential for educators to provide appropriate programs and inclusive opportunities. This paper will describe a three-year project that the Faculty of Education at Brock University has begun with six other international post-secondary institutions in an attempt to cross cultural barriers in the field of advocacy for persons with disabilities. Over the course of two years, 40 Canadian students and 30 European students will be involved in an international course and internship experience. This is intended to be an experientially-based, intensive immersion experience in disabilities instruction. It is expected that the participants will begin to view disability from a human rights perspective and to return home with the cultural knowledge and understanding of disability needed to promote a more inclusive 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)0.010
metaresearch head score (Gemma)0.010
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 score0.303
Threshold uncertainty score0.603

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0250.025
Scholarly communication0.0160.008
Open science0.0020.017
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.213
GPT teacher head0.377
Teacher spread0.164 · 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

Citations1
Published2008
Admission routes3
Has abstractyes

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