Bibliographic record
Abstract
The purpose of our collaborative research is to examine how teachers can effectively accommodate the needs of neurodiverse students in mainstream classrooms. Neurodiversity refers to the idea that we all have unique ways of thinking and interpreting the world around us. It is an inclusive term meant to emphasize that differences are not deficits. This term is often used in the context of neurological, behavioral and developmental conditions. Recent studies suggest that the number of people diagnosed with ADHD have increased 4.1% and autism diagnoses have increased ninefold (Abdelnour et al., 2022). This increase clearly highlights the need for increased teacher education in this field. Our findings have identified numerous strategies to help teachers support the needs of neurodiverse students. Some of these strategies include presenting information in smaller chunks, diversifying teaching methods, and implementing different levels of support in the classroom in order to create a safe and inclusive environment for everyone. Other supports for neurodiverse students include individualized education programs. These documents enable teachers and students to collaborate in developing effective learning plans. Through establishing a student's individual strengths, weaknesses, needs and goals, teachers can effectively support their learning in the classroom. Further research on this topic could explore changes to curricula and teacher education that would better support the needs of all students. Our recommendations aim to help educators provide superior and equitable support to all students throughout their academic careers.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.052 | 0.042 |
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".