Bibliographic record
Abstract
It has been suggested that LIS literature discussing neurodivergence uses undesirable models of disability and undesirable language despite growing advocacy for alternatives. We examine the models and language used in 44 works on neurodivergence and academic libraries and find that 95% of those works use undesirable language like patronising, person-first, and medicalised/deficit-focused language. The medical model is never explicitly used, but numerous works with no explicit model use medicalised/deficit-focused language. Although no works use explicitly ableist language, undesirable language is present even in works using the social model of disability. Recommendations for future research and practice are provided. Plus à découvrir : L'utilisation répandue des modèles et langages indésirables en recherche sur la neurodivergence dans les bibliothèques universitaires RésuméIl a été suggéré que la littérature BSI portant sur la neurodivergence utilise des modèles d'handicap indésirables et du langage indésirable malgré la montée des revendications pour l'utilisation d'alternatives. Nous avons examiné les modèles et le langage utilisé dans 44 études sur la neurodivergence et les bibliothèques universitaires et avons trouvé que 95% de ces études utilisent un langage indésirable, par exemple du langage condescendant, centré sur la personne et médicalisé/axé sur le manque. Bien qu'aucune étude n'utilise du langage capacististe explicitement, du langage indésirable est présent même dans les études qui utilisent le modèle social des handicaps. Des recommandations pour de futurs projets de recherche et des pratiques sont données. Mots-clésBibliothèques universitaires; Handicap; Neurodivergence; Capacitisme; Milieu universitaire
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.700 | 0.457 |
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".