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Record W3110743406 · doi:10.15353/cjds.v9i1.596

Disability Barriers in Academia: An Analysis of Disability Accommodation Policies for Faculty at Canadian Universities

2020· article· en· W3110743406 on OpenAlexafffundvenueabout
Natasha Saltes

Bibliographic record

VenueCanadian Journal of Disability Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAccommodationReasonable accommodationConsistency (knowledge bases)Work (physics)Inclusion (mineral)Political sciencePublic relationsDisabled peoplePublic administrationSociologyPsychologyLawSocial scienceApplied psychology

Abstract

fetched live from OpenAlex

This article examines disability accommodation policies for faculty at 42 Canadian universities. Although universities in Canada are legally required to accommodate disabled employees, fewer than half of all universities have a written disability accommodation policy available. The search for disability accommodation policies revealed that there is a lack of consistency in policy implementation as well as language and content. The analysis revealed that disability accommodation policies contain overtly medical language and provisions that work to isolate disabled faculty by reinforcing the notion of competency as able-bodiedness and emphasizing the entanglement between disability, health and medicine. This article encourages universities to acknowledge their role in establishing accessible and inclusive workplaces and concludes with recommendations aimed at addressing some of the gaps and inconsistencies in disability accommodation policies.

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)0.043
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.934

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.017
Science and technology studies0.0230.006
Scholarly communication0.0090.003
Open science0.0030.006
Research integrity0.0020.003
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 head0.126
GPT teacher head0.410
Teacher spread0.284 · 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

Citations22
Published2020
Admission routes4
Has abstractyes

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Same venueCanadian Journal of Disability StudiesSame topicDisability Education and EmploymentFrench-language works237,207