Bibliographic record
Abstract
Abstract This chapter examines the development of laws protecting major sporting events against ambush marketing in key jurisdictions. It first considers the Olympic Games—from Sydney 2000’s pioneering federal Indicia and Images Act through successive host-nation statutes in Salt Lake City, Athens, Turin, Beijing, Vancouver, London, Sochi, Rio, Pyeongchang and Paris—showing how each adapted and extended trade mark, unfair competition and bespoke criminal provisions to safeguard Olympic symbols, mascots, slogans and related imagery. The chapter then turns to the Commonwealth Games, FIFA World Cups, ICC Cricket World Cups and Rugby World Cups, contrasting hosts that enacted dedicated brand protection statutes (Australia, South Africa, Brazil, Russia, the West Indies) with those that relied solely on generic intellectual-property and competition laws (India, Japan, UK). It highlights recurring themes: a hierarchy of mega events, the spread (vertical creep) of anti-ambush regimes within federations, and the patchy international consensus on adequate protection. Finally, the chapter draws lessons for organisers and legislators on balancing sponsors’ rights, free competition and public interest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.223 | 0.068 |
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".