Profiling Major Sport Event Visitors: The 2002 Commonwealth Games
Bibliographic record
Abstract
It has become common practice for governments and municipalities around the world to bid for the right to host a major sporting event. Prior to embarking on the bidding process, politicians attempt to determine whether such an event will be of value to their municipality; and often focus on the estimated economic consequences of hosting such an event. Frequently, studies are commissioned to predict the event's economic value. However, these studies often miscalculate the potential impact of sport event visitors as consumers. We argue that enhanced profiling of these visitors will enable a more accurate assessment of economic impact. The current research surveys 1,196 spectators of the 2002 Commonwealth Games to demonstrate four important aspects of visitor profiles related to economic impact: (i) visitor typology, (ii) sport tourist behaviors, (iii) consumption patterns determined by interest, and (iv) consumption patterns determined by distance traveled. Overall, the work makes three important contributions to the literature by: (i) empirically supporting that different sports attract different market demographics, (ii) underlining the need for segmentation in economic impact studies, and (iii) identifying the need to develop metrics of economic impact analysis that consider segmentation effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".