Mortality at Music Festivals: An Update for 2016-2017 – Academic and Grey Literature for Case Finding
Bibliographic record
Abstract
In 2016, the authors published a paper on music festival fatalities between the years 1999 and 2014 (n=722). In this Special Report, they provide an update on fatalities reported at music festivals globally for the period 2016-2017 (n=201). Using a search strategy designed to capture grey literature and media reports of music festival fatalities, reports of the overall frequency and cause-of-death breakdown for publicly reported, festival-related deaths are recorded. This update shows an increase in the frequency of festival-related fatality reports during the new period, together with an increase in the number of deaths attributable to terror (n=60) and overdose/poisoning (n=41). Drawing conclusions about the cause of this increase is challenging given the growth in Internet use, online media reports, and number of music festivals occurring annually when compared with the previous reporting period. The authors re-emphasize the need for a uniform reporting standard and reliable epidemiological data for fatalities related to music festivals, mass gatherings, and special events. TurrisSA, JonesT, LundA. Mortality at music festivals: an update for 2016-2017 - academic and grey literature for case finding. Prehosp Disaster Med. 2018;33(5):553-557.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.051 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".