A Neuroinclusive School Model: Focus on the School, Not on the Child
Bibliographic record
Abstract
Inclusive education involves adapting schools upfront to the needs of students with diverse profiles to enable them to fulfill their potential and develop a sense of belonging to their schools. This study aimed to design a model that clarifies the features of school occupations and environments that support the participation and well-being of autistic students and their peers. A research-design approach was used to develop the model. Through the iterative and collaborative process, it came out as necessary for the model to target a larger diversity of students, especially those who are neurodivergent. The model includes nine desirable features of school environments and occupations that support the meaningful participation and the well-being of neurodivergent students. The importance for people in the social environment to celebrate neurodiversity, and to provide safe and caring spaces, came out as particularly critical. The model also provides indications relating to the physical environment (e.g. being free from excessive stimuli) and to the desired features of activities (e.g. harnessing passions and strengths, offering various options). It invites collaboration between stakeholders to create more welcoming schools for neurodivergent students and their peers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads 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".