Experiences from Assessing Safety in Vingis Park, Vilnius, Lithuania
Bibliographic record
Abstract
The aim of this article is to suggest a multi-method approach for assessing safety in parks. The study is based on the analysis of police crime data combined with information from a safety walk and safety survey of park users. The framework is tested in an urban park, Vingis, in the inner city of Vilnius, the capital of Lithuania. Findings show that Vingis is perceived as a safe park, but, compared with police statistics, the survey and the safety walk provide a more nuanced diagnostic of safety in this green area. Vingis’ safety is compromised by car traffic and illicit parking practices, the park’s poor capacity to accommodate users’ needs (e.g. the elderly, parents with small children, young people) and the inadequate infrastructure for users at certain times, such as in the evenings and dark months of the year. Patterns of safety expressed by citizens of Vingis park do not differ from the ones found in parks elsewhere (both in relation to their physical and social environment), despite the recent transformations Vilnius, as the capital city, has overcome since the country’s independence. The article concludes with an assessment of the proposed framework and directions for research and intervention.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".