Ambush by Dre: A Case Study of the National Football League and the Challenges Arising from Conflicting Sponsorship Strategies
Bibliographic record
Abstract
In this case study, students are exposed to the issue of stakeholder management through the lens of the National Football League (NFL), using a contemporary example of ambush marketing and player endorsement deals as the primary context. The case depicts nonfictitious events that involve players and their disdain for league policies regarding donning brands and products that violate exclusivity agreements the league has with other companies. After identifying the origins of the circumstances, the case profiles the three principal stakeholder groups involved (i.e., the players, the ambushed sponsor, and the focal organization) through their respective leaders (i.e., DeMaurice Smith, executive director of the NFL players association, Bob Maresca, president of Bose Corporation, and Roger Goodell, Commissioner of the NFL). Using fictitious commentary, the case culminates with the three actors utilizing the services of a sports consultancy firm as they work together to determine the best course of action. Learning objectives include understanding collegiality in a professional setting, and mitigating conflicting sponsorship strategies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".