Commercial rights management in post-legislative Olympic sponsorship
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the development of preventative counter-ambush marketing initiatives and rights protection strategies, providing an historical view of rights management and the International Olympic Committee’s sponsorship protection initiatives through ambush marketing’s formative years. Design/methodology/approach In examining the antecedents and implications of the Canadian Olympic Committee’s (COC) forward-thinking approach to ambush marketing protection, and to explore the development of preventative counter-ambush initiatives, an historical examination of IOC and COC policies and protocols regarding ambushing and sponsorship protection over a 30-year period was undertaken, informing the development of a proposed model of proactive commercial rights management. Findings The findings indicate that a progressive shift in the counter-ambush activities of major commercial rights holders may be underway: increasingly, the COC has stressed education and communication as key components of their commercial rights protection strategy, in lieu of enforcing the legal protection provided them by the Olympic and Paralympic Marks Act of 2007. The resultant commercial rights management model proposed reflects this proactive approach, and illustrates the need for events and sponsorship stakeholders to Anticipate, (Re)Act and Advocate. Originality/value The study offers a contemporary perspective into counter-ambush strategies within the context of the COC’s brand protection measures and industry practice. The proactive approach to commercial rights management explored represents a significant step in ambush marketing prevention on the part of the COC.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".