Modeling Residents’ Mega Sport Event Social Value: Integrating Social and Economic Mechanisms
Bibliographic record
Abstract
Social value is the difference between monetized social impacts and related economic investments. Stimulating positive social value is a leading concern and focus for sport event stakeholders. However, insights on this socioeconomic phenomenon have concentrated on social or economic mechanisms, not both, and are siloed to host city residents, largely overlooking nonhost city residents central to events. Thus, we integrated social and economic mechanisms to examine host city and nonhost city residents’ mega sport event social value. Data from 1,880 Canadians revealed varying social values (Vancouver and Provincial = negative; Venue-City = neutral; National = positive). Applying a reverse contingent valuation method, findings confirmed the need to integrate (monetized) social and economic mechanisms to calculate social value. Testing an augmented social exchange theory model, findings highlight residents’ perceptual ambivalence to social impacts and the importance of income to estimate social value. Stakeholders should effectively leverage events for social impacts and reconsider event public funding allocation policies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".