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Record W1570259340 · doi:10.4000/terminal.691

Les TIC, les médias sociaux et les étudiants et diplômés canadiens en situation de handicap

2015· article· fr· W1570259340 on OpenAlex
Mai Nhu Nguyen, Jillian Budd, Catherine S. Fichten, Jennison V. Asuncion

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTerminal · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

L’objectif de cette étude est d’explorer l’utilisation des médias sociaux par 133 étudiants et 20 diplômés en situation de handicap des établissements postsecondaires au Canada ainsi que l’accessibilité de ces outils. Les résultats montrent que les trois médias sociaux les plus accessibles pour l’ensemble de l’échantillon étaient Windows Live Messenger (dorénavant intégré à Skype), Blogger et Facebook. Les trois médias sociaux les moins accessibles étaient MySpace, LinkedIn et YouTube. Les données réparties par situation de handicap sont également présentées. En général, les problèmes d’accessibilité sont reliés à l’incompatibilité des technologies adaptées avec certains médias sociaux, à la visibilité et à la mise en page du contenu, ainsi qu’à l’absence de sous-titres. Les participants suggèrent aux développeurs de simplifier la mise en page des médias sociaux et de rendre la navigation plus conviviale.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.189
GPT teacher head0.432
Teacher spread0.243 · 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