The Outcomes of Coattail Marketing: The Case of Windsor, Ontario, and Super Bowl XL
Bibliographic record
Abstract
The recent hosting of Super Bowl XL in Detroit gave the adjacent Canadian city of Windsor, Ontario, the opportunity to ‘coattail market’ the event and reap some obvious benefits. For a modest $250,000 investment, Windsor was billed as the co-host and the local media cooperated by providing very positive coverage of this win win’ arrangement. Using Hiller's (1998) linkage model for analyzing the impact of mega sporting events, the authors use qualitative methods to determine the extent to which the city enjoyed tangible returns as a result of this unique co-hosting arrangement. On-site interviews with selected sport bar owners and a content analysis of local and national newspapers yielded data to support Hiller's model. Viewing mega sporting events in the economic and political context of the host city provides a more balanced landscape from which conclusions might be drawn. In particular, the unexpected outcomes known as parallel linkages are useful in tempering the sometimes exaggerated claims of economic impact that are so often used before, during, and after the staging of these hallmark events.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".