Extending the benefits of leveraging cycling events: evidence from the Tour of Flanders
Bibliographic record
Abstract
Research question: This paper examines event leveraging for public health benefits with the outcome of increasing physical activity participation. While event leveraging provides the foundation for this research, social ecological theory is additionally applied to further examine how leveraging efforts can increase physical activity participation through an understanding of systems and targets. Research methods: An in-depth case study of the Tour of Flanders (Dutch: Ronde van Vlaanderen), Belgium's most popular annual cycling event, is conducted by using qualitative data from interviews and documents. Results and findings: Results reveal that community and sport event-related leverageable resources have been leveraged simultaneously through the strategic use of Flanders' cycling heritage to increase bicycle tourism and active participation in cycling in the region. The Village of the Tour and the Centennial Tour are discussed as two leveraging processes that occur at distinct social ecological systems, using different targets to promote cycling. Implications: The paper argues for greater cooperation between different levels of government operating in the advent of leveraging cycling events to extend the benefits of leveraging.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".