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Record W3122425769 · doi:10.1139/cjp-2014-0306

A survey of the number of legally blind university physics students in Canada during 2003–2013

2014· article· en· W3122425769 on OpenAlexaffvenueabout
A. J. Slavin

Bibliographic record

VenueCanadian Journal of Physics · 2014
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsTrent University
Fundersnot available
KeywordsLegislationMedical educationPsychologyMathematics educationLibrary scienceMedicinePolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Over the last few years, video assistive apparatus has become available at an accessible price that will allow students who have limited vision to participate almost fully in many laboratory courses at university. This paper presents a survey of the number of legally blind university physics students in Canada for the period 2003 to 2013. It will act as a benchmark to measure the effectiveness, in laboratory courses, of recent legislation mandating the provision of assistive devices in educational institutions. The survey was sent to all 52 physics departments at institutions in Canada that grant physics degrees, with all but one of the departments replying. None knew of any legally blind physicists practising in Canada. The only legally blind physics students reported were one partially sighted student who was awarded a Ph.D. in 2001, and one currently at Trent University. The survey results show that of the 12.5% of blind students who hold university degrees in Canada, very few of them are in physics.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.351
Teacher spread0.299 · 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 designObservational
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

Citations61
Published2014
Admission routes3
Has abstractyes

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