Decoding Sustainability Signals: Spectator Perspectives at the 2022 European Championships
Bibliographic record
Abstract
In this study, we aimed to explore event-related signals related to sustainability at the 2022 European Championships in Munich. Drawing upon spectator perspectives and participant observations, we examined how event organizers promoted sustainability at the event, including the contextual and organizational considerations that influenced the interpretations of event sustainability goals. Grounded in signaling theory, the findings revealed that there was a difference in interpretations between bottom-up signals (directly observable) and top-down signals (beliefs and expectations) with respect to the broad understanding of sustainability. These differences were most apparent for spectators concerning social and economic event signals. The directly observable signals emphasized environment-related sustainability, while the top-down signals about social considerations were often overlooked. Event organizers capitalized on some ceremonial moments at the event to bring about greater awareness, but these practices were limited. Event organizers need to consider how to capitalize on both top-down and bottom-up signals to emphasize the sustainability messaging for all stakeholders.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".