Bridging Neurodiversity and Open Scholarship: How Shared Values Can Guide Best Practices for Research Integrity, Social Justice, and Principled Education
Bibliographic record
Abstract
Not all people conform to what is socially construed as the norm and divergences should be expected. Neurodiversity is fundamental to the understanding of human behaviour and cognition. However, neurodivergent individuals are often stigmatised, devalued, and objectified. This position statement presents the perspectives of neurodivergent authors, the majority of whom have personal lived experiences of neurodivergence(s), and discusses how research and academia can and should be improved in terms of research integrity, inclusivity and diversity. The authors describe future directions that relate to lived experience and systematic barriers, disclosure, directions on prevalence, stigma, intersection of neurodiversity and open scholarship, and provide recommendations that can lead to personal and systematic changes to improve acceptance of neurodivergent individuals’ lived experiences within academia.
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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 | 0.295 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.029 | 0.186 |
| Scholarly communication | 0.068 | 0.064 |
| Open science | 0.007 | 0.079 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".