Ambush Marketing and the Right of Association: Clamping Down on Referenced to that Big Event with All the Athletes in a Couple of Years
Bibliographic record
Abstract
Ambush marketing activities – such as advertisements that obliquely reference a major event – have frustrated major sport event organizers and sponsors for years. Nevertheless, these activities, so long as they stopped short of trademark infringement or false advertising, have been perfectly legal. In the last decade, major sport event organizers such as the International Olympic Committee and the Federation Internationale de Football Association have pressured national governments to pass legislation prohibiting ambush marketing as a condition of a successful bid to host an event. Such legislation has already been enacted in the United Kingdom, Canada, South Africa, Australia, and New Zealand, and the statutes in these jurisdictions reveal an emerging right of association. In this paper, the author surveys the evolution of this right and its key features. She offers a critique of this right, and argues that the need for it has never been properly established, and that the legislation is overly broad, does not reflect an appropriate balancing of interests, and may infringe upon the freedom of expression.
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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 | 0.011 | 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 | 0.011 | 0.050 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".