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Record W55104744 · doi:10.1177/0145482x1210600704

Experiences of Students with Visual Impairments in Canadian Higher Education

2012· article· en· W55104744 on OpenAlexaffabout
Maureen J. Reed, Kathryn Curtis

Bibliographic record

VenueJournal of Visual Impairment & Blindness · 2012
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Programs
Canadian institutionsYork UniversityToronto Metropolitan University
Fundersnot available
KeywordsGraduation (instrument)Higher educationPsychologyVisual impairmentMedical educationReading (process)Special educationBlindnessLow visionPedagogyMedicineOptometry

Abstract

fetched live from OpenAlex

Introduction This article presents a study of the higher education experiences of students with visual impairments in Canada. Methods Students with visual impairments and the staff members of disability programs were surveyed and interviewed regarding the students’ experiences in entering higher education and completing their higher education requirements. Results Although the reported graduation rates were high, the students took more than four years to complete their studies. They thought that heavy reading requirements, work in groups, and an inability to participate in some activities were barriers to their full participation in higher education. Discussion The findings demonstrate that barriers exist that have a negative impact on the higher education experience of students with visual impairments. Implications for practitioners Students with visual impairments have challenges that require unique preparation for higher education.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.141
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.004
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.047
GPT teacher head0.433
Teacher spread0.387 · 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

Citations67
Published2012
Admission routes2
Has abstractyes

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