There Can Be No Single Approach for Supporting Students with Autism Spectrum Disorder in Academic Libraries, but Sensory-Friendly Spaces and Clear Policies May Help
Bibliographic record
Abstract
A Review of: Anderson, A. (2018). Autism and the academic library: A study of online communication. College & Research Libraries, 79(5), 645-658. https://doi.org/10.5860/crl.79.5.645 Abstract Objective – To investigate how people with autism spectrum disorder (ASD) discuss their experiences in academic libraries in an online community of their peers. Design – Qualitative content analysis. Setting – Online discussion forum. Subjects – An unknown number of registered members of Wrong Planet (wrongplanet.net), who self-identify as having ASD and have posted about academic libraries on the public discussion board since 2004. Methods – Potentially relevant Wrong Planet public discussion board threads posted between 2004 and an undisclosed collection date were retrieved using an advanced Google search with the search strategy “library; librarian; lib; AND college; university; uni; campus” (p. 648). Each thread (total 170) was read in its entirety to determine its relevance to the study, and a total of 98 discussion threads were ultimately included in the analysis. Data were coded inductively and deductively, guided by the research questions and a conceptual framework which views ASD as being (at least partially) socially constructed. Coding was checked for consistency by another researcher. Main results – Wrong Planet members expressed a variety of views regarding the academic library’s physical environment, its resources, and the benefits and challenges of interacting socially within it. Many members discussed using the library as a place to escape noise, distraction, and social interaction, while other members expressed the opposite, finding the library, its resources, and its patrons to be noisy, distracting, and even chaotic. Social interaction in the library was seen both positively and negatively, with members appearing to need clearly defined rules regarding collaboration, noise, and behaviour in the library. Conclusion – While there is no one-size-fits-all approach to supporting students with ASD in academic libraries, the findings suggest it may be beneficial to provide sensory-friendly environments, designate defined spaces for quiet study and for collaboration, clearly state rules regarding noise and behaviour, and provide informal opportunities to socialize. The author also suggests libraries raise awareness of the needs of ASD students among the entire academic community by hosting events and seminars. The author plans to build on these findings by surveying and interviewing relevant stakeholders.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.026 |
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".