A Study on Road Safety for the Visually Challenged – Policy Implications
Bibliographic record
Abstract
There is an increase in number of accidents, injuries and deaths due to a rise in motor vehicle population. India is a signatory of the Brasilia Declaration with an agenda to reduce the number of accidents by 50% by the year 2020, yet it can be observed that the incidents of accidents have not decreased. There were 285 million visually impaired people in the world, of which 246 million had low vision and 39 million were blind as per the World Health Organization report (2012). It is alarming to note that around 90% of the blind live in the developing world. They are constantly dealing with challenges in their day to day life. Commuting by roads is all the more unsafe for these visually challenged pedestrians. This unique study has conducted an in depth interview to understand the needs and problems faced by the visually challenged pedestrians, followed by a primary survey on World White Cane Day to judge the extent of awareness of the correct technique. The appropriate techniques were then demonstrated to the general public, thus creating awareness and the behavioral changes needed in this endeavor. The paper has used a theoretical concept and practically suggests policy implication for empowering the disabled through awareness drives and collaborating with various government agencies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".