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Record W3097792928 · doi:10.15173/ijsap.v4i2.4051

Students with disabilities as partners: A case study on user testing an accessibility website

2020· article· en· W3097792928 on OpenAlexafffundvenue
Kate Brown, Alise de Bie, Akshay Aggarwal, Ryan Joslin, Sarah Williams-Habibi, Vipusaayini Sivanesanathan

Bibliographic record

VenueInternational Journal for Students as Partners · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsGeneral partnershipInclusion (mineral)Intervention (counseling)Medical educationTest (biology)Order (exchange)PsychologyMedicinePolitical scienceBusiness

Abstract

fetched live from OpenAlex

While partnership is widely encouraged as an approach to advancing the inclusion of disabled postsecondary students, these collaborations are largely taking place between staff offices and failing to meaningfully integrate disabled students as partners. In this case study, we describe the successes and challenges of a pilot project where students and staff with and without disabilities worked together to user test our university’s accessibility website, to which faculty/staff are regularly directed for resources on making their teaching more accessible. We achieved our goal of compiling results into a report for decision-makers in order to advance campus-wide technological accessibility. Instead of primarily treating disabled students as lacking capacities and requiring programmatic intervention to succeed in the university, a partnership approach validates and draws on disabled students’ specific expertise and experience to make institutional change.

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)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0200.010
Scholarly communication0.0070.006
Open science0.0040.015
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.255
GPT teacher head0.622
Teacher spread0.367 · 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 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

Citations19
Published2020
Admission routes3
Has abstractyes

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