Road Safety for Persons with Disabilities in Palembang
Bibliographic record
Abstract
According to the Ministry of Social Affairs No. 178/HUK/2016, there are 245,716 persons with care home-based social welfare problems spread across Indonesia and 5,824 care homes throughout the country. The regional governments, assisted by the central government, build public facilities that can accommodate the needs of citizens with disabilities who are often neglected. One of concrete results of development is when there is equitable development where all elements of the population can feel the impact of the development, including the disability community. Palembang as an organizer of international events still has problems with transportation and facilities for road users. The purpose of this study is to analyze the effect of safety of facilities and infrastructure on public transportation users in Palembang (a case study of persons with disabilities in Palembang). The methods used are quantitative and qualitative and the sampling method used is purposive sampling and 148 saturated samples. Based on the results of the study, it can be concluded that all variables of facilities and infrastructure are proven to be significant, but several variables such as road facilities, pedestrian bridges (JPO) have weak relationships, and persons with disabilities are considerably helped by Trans Musi conductors. Transmission conductors are considered very attentive to the needs of passengers from waiting for arrival to stop. The results of this study are expected to contribute to improving facilities and infrastructure, especially for persons with disabilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".