Fulfillment of Labor Rights for Persons with Disabilities in Indonesia
Bibliographic record
Abstract
The purpose of this research is to fulfill the rights of persons with disabilities to obtain jobs following their fields without reducing their rights. The research method used is normative juridical with literature study. Decent work is a right for every human being without exception. Various racial, ethnic, and religious backgrounds that are part of a human's identity do not become a barrier for him to get his right. Likewise with the physical or non-physical conditions that underlie a human being. Every human being who has a certain physical or non-physical background also has the same rights to get decent work, including persons with disabilities. The State of Indonesia ratified the Convention on the Rights of Persons with Disabilities into Law Number 19 of 2011. In the preamble of the law, it was explained that the countries that signed the convention had the obligation to promote and protect the rights and dignity of persons with disabilities and promote their participation in the civil, political, economic, social, and cultural spheres is based on equal opportunities, meaning that the Indonesian Government is obliged by law to fulfill the rights of persons with disabilities, especially about the right to work in Indonesia.
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 | 0.002 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".