Running the Numbers: Economic Impact and Demand Drivers of U.S. Marathon Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marathon races have a significant impact on local economies. They can give the host city a big economic boost, directly impacting employment and tourism. For example, the 2022 Chicago Marathon generated $386 million for the city of Chicago, almost 3,000 jobs were created, and tourism sent $163 million into the local economy. Because of the importance of a marathon to a local economy, it is important that races optimize demand. This analysis uses a cross-sectional demand model with 31 observations to identify the factors determining a marathon event's impact across the United States. The effect of a marathon event depends on the number of participants, which is a measure of demand. Results suggest that the average price of entry fee, quarter of the year, and number of Instagram followers have a positive and statistically significant effect on the number of marathon participants. The inclusion of a 10k race and the number of Facebook followers have a negative correlation with the number of marathon participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it