Factors of road accident compensation and their relative importance: A case study of India
Bibliographic record
Abstract
Road accidents have become a global problem and have been widely addressed in various reports and research. However, the detrimental impacts of road accidents are rarely discussed. In developed countries, the fatality rate due to road accidents is fairly low and the mechanism to compensate the victims is comparatively robust, on the other hand, the fatality rate due to road accidents in developing countries is quite high and the compensation mechanism is quite abject. In view of the same, this paper is an attempt to look into the various parameters that are to be used to provide compensation to road accident victims. In this paper experts from different domains were selected. which included road safety experts, medical experts, rehabilitation center pundits, economists, sociologists, Insurance experts, and trauma center experts. These experts were interviewed to weigh the parameters of the road accident compensation equation. To evaluate the data and determine the components' ranking, the technique applies the Analytic Hierarchy Process (AHP). Based on the findings of this paper it can be said that the most important factor that needs to be taken into account is victims’ related costs while the least important factor as per the findings of the paper is cost related to property damage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".