Bibliographic record
Abstract
The aim of this feature articles is to clarify how the provisionof reasonable accommodation, which has been made compulsoryfor all business owners in Japan since April 2024, is understood andimplemented at universities in the UK, Canada, Australia and SouthKorea, based mainly on interviews with those involved in providingsupport on a day-to-day basis. In the Western countries of the UK,Canada and Australia, proactive support is provided based on the‘social model’ of disability, whereas in South Korea, where the‘medical model’ still has a strong influence, a support system is inplace that clearly distinguishes between disability and other mentalillnesses. In addition, while the need for consideration and support forstudents with disabilities and special characteristics is widely sharedin all countries, there were differences in what is considered to be‘reasonable’ depending on the social background and circumstancesof each country. In recent years, the number of students withdisabilities that are not easily apparent from appearances, such asmental illness or developmental or learning disabilitie, has beenincreasing rapidly. In universities, there is a need to share the idealssuch as inclusive education and universal design for learning, butthere is also a need for further consideration of how to guarantee fairand equitable learning opportunities for individual students, as well ashow to guarantee the quality of educational standards.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".