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Record W3001383123 · doi:10.24908/pceea.vi0.13700

A MULTIDISCIPLINARY LEARNING EXPERIENCE FOR EDUCATION IN ACCESSIBILITY

2019· article· en· W3001383123 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsCarleton University
Fundersnot available
KeywordsMultidisciplinary approachPerspective (graphical)Engineering ethicsContext (archaeology)LegislationEngineeringPedagogyPsychologyPolitical scienceSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Abstract – This paper argues that accessibility is both a complex issue and one in which engineering students would deeply benefit from learning more about due to mounting legislation and demand. Engineers can play an important role in creating a more inclusive society, yet questions remain as to how to instill knowledge of accessibility effectively in the classroom. 
 To answer these questions, the authors explore and draw preliminary conclusions from a multidisciplinary learning experience they designed by which two upper level undergraduate courses from engineering and history were joined on separate occasions to provide some education in accessibility.
 Although crafting and delivering this experience posed some challenges, the authors believe that this multidisciplinary approach generally enriched the learning experience for students, exposing them to a broader perspective, and in particular the historical context for accessibility initiatives and the lived experience of people with disabilities.

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.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.292
Teacher spread0.283 · 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