Examining Monetary Valuation Methods to Analyze Residents' Social Value From Hosting a Publicly-Funded Major Sport Event
Bibliographic record
Abstract
Measuring residents' social value from hosting major sport events has become a popular practitioner and researcher focus. However, researchers have used a plethora of monetary valuation methods to measure social value on an equally diverse set of events. Rather than being applied to major sport events, the use of these methods in sport research has been limited to smaller events, programs, or facilities. Consequently, investigating monetary valuation methods for major sport events is necessary to inform practitioners and researchers of these types of events as to which tool(s) to use. Thus, the purpose of this study was to investigate various monetary valuation methods to determine which method(s) is(are) best to examine residents' social value in a post-event context and test the selected method(s) for the 2010 Olympic Winter Games in Vancouver, Canada. After reviewing monetary valuation methods found in the sport management literature, two methods were deemed suitable avenues to pursue: the reverse contingent valuation method and the opportunity cost approach. This study employed an exploratory sequential mixed methods design to derive a conceptual and empirical analysis. Interviews were conducted with 14 Vancouver residents and supplemented with document analysis; as well, 525 Vancouver residents completed a self-administered online survey. Findings highlighted the importance of using both the reverse contingent valuation method and opportunity cost approach given their complementary nature. The reverse contingent valuation method allowed residents to select how much they valued their experience. This individual or micro-economic perspective is a necessary prerequisite for residents to adequately determine their value of hosting in relation to other options (e.g., building hospitals, having professional sport teams) when applying the opportunity cost approach, which asks residents to reflect at societal or macro-economic level. This synergistic approach demonstrates the importance of addressing both perspectives: the micro (i.e., individual exchange) and the macro (i.e., event exchange) aspect. In doing so, this approach offers researchers and practitioners avenues forward to examine the social value of publicly-funded major sport events exclusively through a direct, an indirect, and a synergistic method to advance the examination of major sport events' social value.
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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 | 0.035 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".