Ambush Marketing Legislation to Protect Olympic Sponsors: A Step Too Far in the Name of Brand Protection?
Bibliographic record
Abstract
Ambush marketing and its possible threat to brand equity has been identified as a key concern for mega sport event organisations and their sponsors. In recent years, international sport federations have sought to leverage the enormous interest in hosting their events in order to make anti-ambush marketing legislation a requirement for a successful bid. While sponsors may applaud such added protection, there are potentially a number of negative impacts that deserve consideration when discussing ambush marketing legislation.In this paper, we examine the growing trend for mega sporting event organizers to insist upon the enactment of legislation to protect against ambush marketing. Using the Olympic Games as a model, we provide a brief overview of the Olympic brand, Olympic sponsorship and the brand management/protection strategies developed by the IOC. Our study pays particular attention to the Vancouver Olympic Committee (VANOC) for the 2010 Olympic Winter Games and to the legislation enacted by the Canadian government to protect the Olympic and Paralympic brands. We examine some of the issues that arose in relation to the Vancouver Games, and discuss the impact of the growing use of anti-ambush legislation as the ultimate weapon to protect sponsors. While more research is needed to assess fully the impact of such legislation on various stakeholders, there are signs that perhaps it can do more harm than good.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".