Building Neurodiversity-Inclusive Postsecondary Campuses: Recommendations for Leaders in Higher Education
Bibliographic record
Abstract
Neurodivergent people are increasingly involved in postsecondary education, but they continue to face serious barriers and challenges on college campuses.These challenges are not only related to disability functional differences and accommodation needs, but also to stigma and prejudice toward neurodivergent people.Consequently, neurodivergent people are less successful than neurotypical peers; moreover, intersections between neurodivergence and other marginalized groups are associated with even greater inequities.This article was written by neurodivergent students and researchers, and their allies, who suggest a system-wide approach is needed to promote inclusion of neurodivergent students, staff, and faculty on postsecondary campuses.Specific recommendations, based on those the authors suggested to and that were endorsed by the University of California Academic Senate, are provided.These recommendations include diversity, equity, and inclusion (DEI)-oriented reforms (viewing neurodiversity through a DEI lens; establishing Disability Cultural Centers; providing campus-wide neurodiversity training; and fostering neurodivergent leadership in neurodiversity initiatives).Other recommendations address disability accommodations and supports (integrating disability accommodations in one place; making eligibility requirements less onerous; recognizing and accommodating sensory distress and distraction; establishing programs to facilitate transitions in and out of postsecondary; improving mental health support; and creating mechanisms to resolve issues where students are denied accommodations).Finally, further recommendations address accessibility of communication (respecting students' decisions to involve support people; and offering neurodivergent people the option to choose accessible modalities for communicating with instructors and staff and for taking classes).Institutions that embrace these reforms have an opportunity to position themselves as neurodiversity inclusion leaders and destination campuses for neurodivergent people.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".