Opening the ‘black box’ of building mass sport and physical activity participation from major sporting events: developing a process model of event inspiration
Bibliographic record
Abstract
Research question The paper examines the growing research interest in event inspiration, specifically the assumption of building mass sport and physical activity (SPA) participation through major sporting events (MSEs). It aims to clarify the processes through which inspiration can be cultivated as a first step to form SPA intention and behaviour from MSEs.Research methods Insights from psychology, event management, health behaviour, and sport studies literature were integrated to develop a process model of event inspiration.Results and findings The study argues that ‘being inspired by’ is different from ‘being inspired to’; and MSEs are one of several sufficient but not necessary causes for building SPA participation. Personal characteristics at the micro level and contextual conditions at the macro/meso levels also influence the potential for event inspiration. A psycho-behavioural process model is subsequently proposed, highlighting the dynamic interplay between event inspiration, event leveraging, and behaviour change, emphasising that leveraging efforts should align with the timing of inspiration – either pre-, during, or post-event – and account for the multi-stage behaviour change process (willingness, intention, and action), rather than adhering to traditional event hosting phases. This model suggests that these processes should work in tandem to achieve the intended inspirational effects of MSEs.Implications The paper offers a significant conceptual contribution to understanding the potential of MSEs to promote more active lifestyles within the general population. The new theoretical model marks a step change in our understanding of ‘inspiration’ in the context of MSEs that contributes to future development in research and practice.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".