Modeling Resident Spending Behavior During Sport Events: Do Residents Contribute to Economic Impact?
Bibliographic record
Abstract
The role of residents in the calculation of economic impact remains a point of contention. It is unclear if changes in resident spending caused by an event contribute positively, negatively, or not at all. Building on previous theory, we develop a comprehensive model that explains all 72 possible behaviors of residents based on changes in (a) spending, (b) multiplier, (c) timing of expenditures, and (d) geographic location of spending. Applying the model to Super Bowl 50 indicates that few residents were affected and positive and negative effects were relatively equivalent; thus, their overall impact is negligible. This leaves practitioners the option to engage in the challenging process of gathering data on all four variables on all residents or to revert back to the old model of entirely excluding residents from economic impact. From a theoretical perspective, there is a pressing need to properly conceptualize the time variable in economic impact studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".