Ways to Improve Road Safety Audit in the Republic of Kazakhstan
Bibliographic record
Abstract
There are two types of internationally recognized engineering approaches to addressing road safety issues – proactive and reactive. Predicted or proactive approach includes prevention of accidents and taking corrective measures before accidents can occur. One example of this approach is road safety audit, which is a relatively new tool in developing countries. The paper has analyzed approaches to road safety audit outlined in guidelines of the United Kingdom, Ireland, USA, Australia, Canada, as well as in guidelines of Asian Development Bank for the countries of Central Asian Regional Economic Cooperation. All reviewed documents are characterized by a similar approach to the basic definitions, to selection of audit team and requirements for team members, to the main stages of road safety audit. All reviewed guidelines emphasize that audit is not a means of project works evaluation, verification of compliance with standards, a means of projects ranking or evaluation. Approach to road safety audit, outlined in recommendations developed in the Republic of Kazakhstan has also been analyzed. It has been established that the document does not meet approaches adopted in developed countries. Conditions of the Republic of Kazakhstan require development of a new document based on analysis of the best international experience. Currently, the Republic of Kazakhstan has embarked on a gradual introduction of “road forgiving mistakes” approach to the design and operation of roads. This fact should also be considered when revising road safety audit manual.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".