Exploring Experiences in Event Management Under Uncertainty: The Four “Knowns” Framework
Bibliographic record
Abstract
This study explores event managers’ experiences under pandemic-driven uncertainty, focusing on the Kyoto Marathon and the Osaka Marathon during the COVID-19 pandemic. Grounded in critical realism, we thematically analyzed data from archival materials (9,453 archival pages) and 14 interviews with secretariat members, and identified five key experiences: (1) Difficulty in ensuring safety , (2) A trade-off between empty expenses and accurate judgement , (3) Sponsor considerations , (4) Concern about reputational damage , and (5) Conflict between institutional logics and stakeholders’ organizational logics . We then compared these findings with existing knowledge, using the four “knowns” framework on uncertainty, which consists of: Known–Knowns, Known–Unknowns, Unknown–Knowns, and Unknown–Unknowns. Findings highlight the dynamic nature of pandemic-driven uncertainty and the overlooked state of Unknown–Knowns. These theoretical and conceptual insights offer implications for both researchers and practitioners in event management to better prepare for future uncertainty.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".