Neurodiversity and academic integrity: toward epistemic plurality in a postplagiarism era
Bibliographic record
Abstract
Academic misconduct policies can disadvantage neurodivergent students through ableist assessment design, surveillance technologies, and pedagogies of forced disclosure. In this narrative review, the intersection of neurodiversity and academic integrity in higher education was examined by analyzing 15 sources, using postplagiarism as a conceptual framing. Three themes emerged: (1) neurodivergent students face intersectional challenges when academic integrity frameworks misinterpret their cognitive differences as misconduct indicators; (2) educational technologies present a double impact: AI can improve accessibility, but detection software produces false positives disproportionately affecting neurodivergent students; and (3) competitive assessment practices foster environments where misconduct becomes more likely. Epistemic plurality is proposed as a framework to reconceptualize academic integrity beyond punitive approaches. Rather than standardizing knowledge expression, we can recognize that diverse cognitive styles enrich, rather than compromise, academic quality. Academic integrity must evolve beyond compliance-based models toward inclusive approaches that honour neurodiversity as human variation that enriches academic communities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrityScience and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Research integrity Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.061 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".