The 2022–2023 <i>Event Management</i> Journal State of Play Review
Bibliographic record
Abstract
This article provides the state of play of Event Management since the current editorial team took over in November 2021 until manuscripts accepted in September 2023. Our bibliometric analysis indicates 234 distinct authors from Africa, the Americas, Asia, Europe, the Middle East, and Oceania contributed to the journal. Together with the varied theories and perspectives used to ground the research, the international nature of our authors demonstrates the growth, maturity, and robustness of event studies published in Event Management . Though sport events dominated, festivals, conferences/private events, and mixed or industry-wide studies were also published. Five meta-themes emerged: the event industry, social impacts and sustainability, destination image, temporal and contextual factors, and performance. The focus on the human and social elements is striking and welcome in this postpandemic era. Finally, we identify submission gaps, offer future research directions [e. g., artificial intelligence (AI)/technology and sustainability], and suggestions to strengthen the journal and field.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".