Bibliographic record
Abstract
Neurodiversity is a concept and a social movement that addresses and normalizes human neurocognitive heterogeneity to promote acceptance and inclusion of neuro-minorities (e.g., learning disabilities, attention disorders, psychiatric disorders, and more) in contemporary society. Neurodiversity is attributed to nature and nurture factors, and about a fifth of the human population is considered neurodivergent. What does neurodiversity mean neuroscientifically? This question forms the foundation of the present entry, which focuses on existing scientific evidence on neurodiversity including neurodiversity between and within individuals, and the evolutional perspective of neurodiversity. Furthermore, the neuroscientific view will be synergistically integrated with social approaches, particularly in the context of the normalization of neurodiversity and its association with the medical and social models of disability. This multidimensional analysis offers a cohesive and comprehensive understanding of neurodiversity, drawing insights from various vantage points, such as social, psychological, clinical, and neuroscientific viewpoints. This integrated approach fosters a nuanced and holistic discussion on the topic of human diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".