Exploring Experiences in Event Management Under Uncertainty: The Four “Knowns” Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it