Bibliographic record
Abstract
The information structures, styles and priorities of those in the disabled community are vastly different from those that make up academic culture, creating a disparity between the needs of disabled students and the support of the university accommodations system. With mixed methods data that describes the post-secondary accommodations system, a model of overlapping frames of information access and use could be created that would allow for an in-depth analysis of information and the disabled post-secondary experience. In addition to contributing to the literature on post-secondary disability and accessibility, this research will also produce concrete recommendations to improve post-secondary accommodations systems. Encadrer le handicap: comprendre l'avenir de l’accommodement pour les étudiants postsecondaires en consignant les expériences passées RésuméLes structures d'information, de styles et de priorités des personnes handicapées sont très différentes de ceux qui constituent la culture universitaire, ce qui crée une disparité entre les besoins des étudiants handicapés et le soutien du système d'accommodement de l'université. Grâce à des données issues de méthodes mixtes décrivant le système d'accommodement de l'enseignement postsecondaire, il est possible de créer un modèle chevauchant accès à l'information et utilisation de l'information, ce qui permettra une analyse approfondie de l'information et de l'expérience des étudiants handicapés dans l'enseignement postsecondaire. En plus de contribuer à la littérature sur le handicap et l'accessibilité au niveau postsecondaire, cette recherche produira également des recommandations concrètes pour améliorer les mesures d'accommodement au niveau postsecondaire. Mots-clésInteraction humain-information; études sur le handicap; interaction humain-machine; enseignement postsecondaire; études sur les politiques; études sur les accommodements; EDIA
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.099 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".