Surveillance, trust, and policing at music festivals
Bibliographic record
Abstract
Music festivals are often the highlight of summertime, but they are also spaces increasingly policed for drugs, pickpockets, sexual assault, and terrorist attacks. The pop‐up nature of festival spaces creates a tension between organizers ensuring safe environments and festival‐goers seeking community and fun. We conducted an online survey of festival‐goers to determine their safety concerns and feelings about security measures. The biggest safety concern was authorities, including police, private security, and surveillance. We found significant differences between males and females. Females had more concerns about personal safety and males had negative attitudes about surveillance and security—perhaps reflecting a male privilege. The negative attitude towards surveillance and police was common across demographic groups but stronger in males. A striking finding is that 87% of our participants felt that the ethos of a festival best creates a feeling of safety, while surveillance changes the nature of these public spaces—56% of our respondents felt it creates a bad vibe and 44% said it causes anxiety. We speculate that this sentiment parallels the Defund the Police movement following the Black Lives Matter protests in the United States—community is key to a safe city and surveillance is viewed as creating negative spaces .
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".