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Record W3001579566 · doi:10.24908/pceea.vi0.13687

Research and Education in Accessibility, Design, and Innovation (READi) : A Reflection of Our First Year

2019· article· en· W3001579566 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.
fundA Canadian funder is recorded on the work.
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 institutionsQueen's UniversityTetra Society of North AmericaCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInclusion (mineral)FeelingReflection (computer programming)Medical educationPsychologyProgram Design LanguagePedagogyEngineeringMedicineSocial psychologyComputer science

Abstract

fetched live from OpenAlex

Research and Education in Accessibility, Design, and Innovation (READi) is an interdisciplinary training program focusing on accessibility. With the first year of the READi completed, this paper provides an overview of the design of the program and reflections from the program, as experienced by two of its trainees. The training program appears to have increased the knowledge and skills of student trainees with regard to accessibility, while also enhancing many professional skills. In addition, there appears to be affective learning, uplifting the thoughts, opinions, and feelings of accessibility and inclusion, that foster a culture of accessibility. The program benefits from interdisciplinarity, collaborations with external stakeholders, engagement with real-world accessibility issues, and inclusion 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)0.003
metaresearch head score (Gemma)0.003
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 score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.037
GPT teacher head0.347
Teacher spread0.310 · 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