Examining Evidence of a Trickle-Down Effect in Multiple Host Country Contexts: UEFA Euro 2020
Bibliographic record
Abstract
This article examines the trickle-down effect (TDE) phenomena in relation to the 2020 UEFA European Football Championship (Euro 2020). The cohosting format of this event, together with the availability of consistent data from the Eurobarometer (population-level data per country) on sport and physical activity participation across multiple jurisdictions, presented a unique research opportunity. Using preevent (2017) and postevent (2022) Eurobarometer surveys, we tested the main mechanisms by which TDEs are theorized to occur through quantitative secondary data analysis. The findings from our study provide tentative evidence in support of a TDE in some Euro 2020 cohost countries, but a direct cause and effect relationship is difficult to establish. We contend that the combination of hosting and success can contribute to protecting against declines in participation at population level. Our findings highlight the most predominant wicked problem for TDE research—the best available data is not always sufficient to evaluate TDEs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".