Bibliographic record
Abstract
Introduction: Fatalities at music festivals are seldom reported in the academic literature, making it difficult to understand the full scope of the issue. This gap in our knowledge makes it challenging to develop strategies that might reduce the mortality burden. It is hypothesized that the number of fatalities is rising. Building on earlier research, two further years of data on mortality at music festivals was analyzed. Methods: Synthesis of grey/academic literature. Results: The grey literature for 2016-2017 documented a total of 201 deaths, including both traumatic (105; 52%) and non-traumatic (96; 48%) causes. Deaths resulted from acts of terror (n = 60), trampling (n = 13), motor-vehicle-related (n = 10), thermal injury (n = 6), shootings (n = 5), falls (n = 4), structural collapses (n = 3), miscellaneous trauma (n = 2), and assaults (n = 2). Non-traumatic deaths included overdoses/poisonings (n = 41), miscellaneous causes (n = 36), unknown/not reported (n = 18), and natural causes (n = 1). The majority of non-trauma-related deaths were related to overdose (44%). No academic literature documented fatalities that occurred while attending a music festival during 2016 or 2017. Discussion: Reports of fatalities at music festivals are increasingly common. However, the data for this manuscript were drawn primarily from media reports, a data source that is problematic. Currently no rigorous reporting system for fatalities exists. In the context of safety planning for mass gatherings, a standardized method of reporting fatalities would inform future planning and safety measures for festival attendees. The hypothesis that mortality rate reporting increased was substantiated. However, the proliferation of music festivals, the increase in attendance at these events, and the overall increase in internet usage may have influenced this outcome.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".