Global Event Data Research Registry: Taking Mass Gathering Research to the Next Level
Bibliographic record
Abstract
Introduction: Research on events and mass gatherings is hampered by a lack of standardized and central reporting of event data and metrics. While there is work currently being done on report standardization, this will require a plan for recording, storing, and safeguarding a repository of event data. A global event data registry would further the work of standardized reporting by allowing for the collection and comparison of events on a larger scale. Aim: To characterize the considerations, challenges, and potential solutions to the implementation of a global event data registry. Methods: A review of the academic and grey literature on the current understanding and practical considerations in the creation of data registries, with a specific focus on an application to mass gathering events. Results: Findings were grouped under the following domains: (1) stakeholder identification and consultation, (2) research goals and clinical objectives, (3) technological requirements (ie hosting, format, maintenance), (4) funding (budget, affiliations, sponsorships), (5) ethics (privacy, protection, jurisdictions), (5) contribution facilitation (advertising, support), and (6) data stewardship and registry access for researchers. Conclusion: This work outlines key considerations for undertaking and implementing an event data registry in the mass gathering space, and compliments ongoing work on the standardization of data collected at mass gathering events. If practical and ethical considerations are appropriately identified and managed, the creation of an event data registry has the potential to make a major impact on our understanding of events and mass gatherings.
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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 | 0.396 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.036 | 0.066 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".