Feeling Safe While Having Fun? Review of Experienced Safety and Fear of Crime at Events and Festivals
Bibliographic record
Abstract
Abstract Events and festivals are big business. Despite differences, the overall goal of providing visitors with a positive experience and making a profit for the organization of the event or festival is the same. As clear liminal settings, events and festivals trigger the experience of freedom among visitors, but research also indicates that this comes at a price of heightened risk of, for example, ‘(...) pickpockets, sexual assault, and terrorist attacks (...)’ (Hoover et al., The Canadian Geographer/Le Géographe canadien 66:202, 2022). At the same time, there is little research attention for how such risks of crime victimization are experienced, and how safe people feel at events and festivals more generally. This is somewhat surprising because, in general, safety is considered to be crucial to the success of (semi)public spaces and people’s willingness to frequent these. One could hypothesize a similar importance to event and festival settings (Dewilde et al. Journal of Peace Education 18:163–181, 2021) and some authors (Pivac et al. Journal of the Geographical Institute “Jovan Cvijić” SASA 69:123–134, 2019; Barker et al., Journal of Travel Research 41:355–361, 2003) claim the experience of safety to be crucial for the future of events. In this chapter we will explore what is special and (potentially) unsafe about events and festivals and review what is known about event and festival visitors’ fear of crime and explanatory factors. Findings are contrasted with knowledge from the general fear of crime literature. In doing so, we pay special attention to gender differences in the experience of fear of crime at events and festivals, the role of environmental factors, and the role of surveillance and policing. Based on our exposition, it follows that there clearly is no one-size-fits-all solution for the prevention of fear of crime at events and festivals, and a practical approach has to be based on tailor-made analyses for specific events and festivals. Increased security and surveillance are not per se the answer to fear of crime at events and festivals; in particular circumstances these might even alarm visitors about the risks of crime victimization, affecting their experienced safety in a negative way. It can also be questioned to what extent such an approach is sensitive to recognizing and addressing the (perceived) threat of sexual harassment and violence, which the literature we reviewed consistently conveys as a specific and pressing risk at events and festivals, especially to women. A way forward could be raising awareness of sexual violence and harassment among visitors, staff, and organizers of events and festivals. We would also argue monitoring perceived risk of different types of victimization (among which sexual harassment and violence) could be expanded using different techniques, such as app-based measurements of real-time experience of safety. In general, it seems that the prevention of fear of crime at events and festivals needs a bottom-up, tailor-made approach, in which technological solutions may play a role but should not be considered a one-size-fix-all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".