Impact studies in sport: the development of an assessment process model
Bibliographic record
Abstract
Purpose The criticisms put forward against economic impact studies lead to a key question: “Is it possible to measure the impact of sporting properties and events in a holistic, conservative, and reliable way?” This research endeavors to build on the academic literature to add to the scope and rigor of economic impact research by proposing an impact assessment process model for practitioners that facilitates employment of a holistic, conservative and reliable impact study and seeks to address these concerns. Design/methodology/approach Using seven identified key realities that highlight the challenges facing impact studies, and adopting a collaborative self-ethnographic methodological approach, the work highlights lessons learned from four empirical economic impact studies undertaken by the authors over a five-year period. Findings The study provides a broad view of impact studies, which extend beyond financial implications and provides a more inclusive methodology. Particularly, the proposed impact assessment process model seeks to improve the credibility of impact studies by facilitating a holistic approach that incorporates direct, indirect and intangible impacts. Research limitations/implications The proposed model has value to researchers and is designed to improve the overall credibility of economic impact methodology. It also provides a more accurate measure of direct impact while considering intangible and indirect impacts, including social/community impacts. Practical implications The proposed model has value to and practitioners and is designed to improve the overall credibility of economic impact methodology. It also provides a more accurate measure of direct impact while considering intangible and indirect impacts, including social/community impacts. Originality/value The proposed process model to measure the impact of a sports event is a needed element in the world of funding, managing and implementing events of all sizes.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".