Нормативно-правові засади безпеки дорожнього руху у зарубіжних країнах
Bibliographic record
Abstract
The article attempts to criminologically study the legal framework for road safety and transport operation in foreigncountries. Dozens of strategies and programs became the empirical basis of the study. Scientific material and the resultsof knowledge of this issue are based on the author’s understanding of certain methodological components. The classificationof the announced normative legal acts according to the scale of their action depending on certain levels, namely:international legal (global), regional, national, local, is carried out. It is emphasized that the legislation of individualcountries in this area can not be considered independently of other levels of regulatory support.A number of strategies, programs and plans adopted over the past two years at the international legal and Europeanlevels in some countries (Sweden, Ireland, Germany, USA, Australia, Canada, England, Poland, Malaysia), localcommunities around the world, and also their features are allocated. The specifics of the concept of zero mortality onVision Zero roads are indicated. The most typical and effective standards of transport safety are singled out, which allowto significantly reduce mortality and injuries from road accidents. The pan-European requirements for road safety havebeen named as a guideline for further improvement in Ukraine. The subjects of development and adoption of the consideredlegislation at all four analyzed levels are listed. The importance of research on this issue is indicated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".