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Record W2268017233

Ambush Marketing Legislation to Protect Olympic Sponsors: A Step Too Far in the Name of Brand Protection?

2014· article· en· W2268017233 on OpenAlexaffabout
Teresa Scassa, Benoît Séguin

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAmbush marketingLegislationHarmBusinessAdvertisingLeverage (statistics)Political scienceMarketingPublic relationsLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.272
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes2
Has abstractyes

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