Legal Protection For People With Disabilities In Terms Of Fulfilling The Right To Education In Higher Education (Case Studies Of Aw In Indonesia And Ra In Canada)
Bibliographic record
Abstract
The urgency of this research is because the reality in the field shows that people with disabilities still face various obstacles in accessing higher education, both in terms of infrastructure, policies, and social acceptance. Legal protection must be given to anyone without exception, including people with disabilities. Persons with disabilities are part of a society that has equal rights in various aspects of life, including education, employment, health, and social participation. One of the rights of people with disabilities that is the main issue in this study is the right to education. In this study, the author uses normative juridical legal research methods or literature research. The results of the study show that Regulations in Indonesia and Canada both guarantee the right to education for people with disabilities, but the implementation is different. In Indonesia, Law No. 8 of 2016 and Permendikbud No. 46 of 2014 have regulated the obligations of universities in providing accessibility, but do not have binding technical standards and strong supervisory mechanisms. Meanwhile, Canada has more comprehensive regulations such as the Accessible Canada Act of 2019, which not only establishes rights but also regulates funding, supervision, and sanctions for institutions that do not comply with the rules. In Indonesia, weak technical standards, lack of infrastructure, and lack of funding are the main obstacles to realizing optimal inclusive education. Meanwhile, in Canada, despite more advanced systems, challenges such as uneven distribution of funds and gaps in facilities in remote areas still need to be addressed. From the results of the study, it can be concluded that legal protection for persons with disabilities in fulfilling the right to education in higher education has been regulated both in Indonesia and Canada, but with a different approach. In addition, that although regulations are in place, the main challenge lies in the implementation of policies at the university level. From this conclusion, the author can provide suggestions that there is a need for policy improvements in both countries so that the implementation of inclusive education can really run effectively and evenly for all students with disabilities Keywords: Legal Protection, Disability, Right to Education
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".