Universal Design for Learning to Support Access to the General Education Curriculum for Students with Intellectual Disabilities
Bibliographic record
Abstract
The issue concerned with enhancing support to the intellectually disabled students for enabling them to access thegeneral education has gained significant importance in the recent years all over the world. The intellectually disabledstudents suffer from neurodevelopmental disorders that acts as a barrier to the normal functioning of the brain andslow down the learning abilities and proper development of an individual. The presence of intellectual disabilitiesaffects both the mental and physical well-being of the students by causing issues for them to understand, thinklogically, speak, remembering things, and find solutions to the problems. Many research studies are conducted acrossthe world for finding the ways and designing innovative models that can help in increasing the access to generaleducation for these students with special needs. The universal design for learning framework also aims at providingsupport to the intellectually disabled students for gaining access to general education by enhancing their intellectualfunctioning and ability to adapt.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".