Legal Aspects of Ensuring Security When using Personal Electric Transport in Russia and Abroad
Bibliographic record
Abstract
Transport plays a fundamental role in the life of society. The fast pace of life, especially in metropolises and cities, imposes new requirements towards human mobility. With the development of technologies unprecedented transport solutions have become popular. Specifically, in different countries personal electric transport (segway, self-balancing scooter, electric scooter, unicycle etc.) has assumed widespread use. The number of such vehicles is constantly growing. As known, any means of transport presents a hazard. That is why it is important to pay special attention to personal security when using this kind of transport. Based on the analysis of the current Russian and foreign legislations, case materials, scientific sources, the article investigates legal problems of ensuring personal security when using personal electric vehicles. In this respect, the authors consider the issues of ensuring safety of both a driver and a pedestrian, and third parties as well. The conducted legal research has allowed us to make a conclusion on the necessity of improving legislation in the sphere of using personal electric transport. In the authors’ opinion, one should start with statutory recognition of the very notion of “personal electric transport”, which must include characteristic features that allow to differentiate between this particular kind of transport and other vehicles. Nothing but comprehensive legal regulation based on a detailed analysis of possible risks, can prevent personal security hazards when using personal electric transport.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".