Bibliographic record
Abstract
The subject of this paper is the examination of the connection between national cultural characteristics and urban safety, i.e. the safest cities, rated as such according to the Safe City Index. Namely, cultural differences determine the different functioning of states in many fields, so academic literature on urban security often investigates whether and in what way cultural variables reflect on the resilience of cities, urban planning, urban development, etc. The theoretical basis of the paper is the theory of cultural dimensions, developed by Geert Hofstede. Therefore, this paper aims to determine whether and to what extent the dimensions of national culture, according to Hofstede, are present in the safety plans of the safest cities according to the Safe City Index and whether the high level of urban safety of the examined cities, also implies the similarity of the national cultures of the countries in which they are located. In the methodological sense, the paper is based on a content analysis respectively qualitative analysis of the safety plans of three selected cities - Toronto, Sydney and Barcelona. The selection was made on whether the highly ranked cities have a safety plan or strategy to make a comparison possible and whether there are findings about the cultural characteristics of the countries in which these cities are located, i.e. whether they are included in Hofstede's research. The analysis showed that similar cultural characteristics characterize the safest cities. Namely, these documents are characterized by inclusiveness, civil participation, and predominantly collectivist orientation, as well as focus on citizens' quality of life. At the same time, the identified cultural dimensions in their safety plans are similar to the established national cultural characteristics, according to Hofstede, for the countries where the given cities are located. Of course, cultural factors are only one of many that should be considered when studying urban safety. Still, there is no doubt that approaches of different societies to risk, uncertainty, prevention, safety and quality of life, are largely culturally determined. Although Hofstede's theory also has limitations, there are undoubtedly many reasons for studying urban safety through the prism of cultural values, considering the potential of Hofstede's model and the theoretical implications that can be reached.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".