Accessibility of the urban environment for people with limited mobility using the example of Arkhangelsk
Bibliographic record
Abstract
The aim of the study is to assess the availability of urban space for people with limited mobility (PLM). Research objectives: - assessment of urban areas - determining the level of accessibility of the urban environment by assigning accessibility indices to certain territories (quarters); - testing using the example of separate quarters of the city of Arkhangelsk. Two non-adjacent city blocks located in its central part were selected for testing. According to the results of the study, it is possible to conclude that the availability of the urban environment for PLM in the selected quarters is: 37.2% for the first quarter, 42.1% for the second quarter. There is no comprehensive infrastructure suitable for PLM in the territory. Among the main problems are poor coating of footpaths, barriers in the form of curbs, the absence of railings on stairs, canopies and ramps, as well as the lack of equipped recreation places. A feature and advantage of the method is that the purpose of the study is not just a description of the quality characteristics of accessibility, but the determination of specific indicators of the security of various complex components of a comfortable urban environment. Integrated monitoring of the quality of the urban environment for PLM will allow the implementation of targeted programs and activities, taking into account their relevance to the expected effects. This contributes to the prudent use of financial resources. The totality of development programs and measures for individual territories will determine the development strategy of the city as a whole.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".