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Record W1164916094 · doi:10.16997/eslj.28

Legal Guarantees for Olympic Legacy

2016· article· en· W1164916094 on OpenAlexaffabout
Stephen A. Stuart, Teresa Scassa

Bibliographic record

VenueEntertainment and Sports Law Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLegislationSustainabilityAmbush marketingBusinessHost (biology)Sustainable developmentPublic relationsPolitical sciencePublic administrationAdvertisingLaw

Abstract

fetched live from OpenAlex

Such is the power of the International Olympic Committee (IOC) that they require sovereign states, eager for key cities to host future editions of the Olympic Games, to enact specific legislation designed to protect, amongst other things, the Olympic brand and its associated trademarks; it is also intended to prevent the occurrence of ambush marketing before and during the period of the Games, thereby protecting the commercial interests of the IOC and the sponsors participating in the Olympic Partner program. However, there are no such requirements for guarantees pertaining to the intended legacy outcomes of these Games, either in terms of their physical manifestation, or with regard to their sustainability, despite sustainable development being one of the pillars of the Olympic Movement. This paper argues that if the IOC were serious in their professed intent that Games’ legacies be beneficial for the residents of host cities, regions and countries over time, they could require the enactment of straightforward legislation guaranteeing planned and sustainable outcomes. Furthermore, the paper provides the example of a Canadian educational foundation to illustrate the infrastructure the IOC could impose on host cities to ensure Games’ outcomes are sustainable in terms of the lifespan of venues, infrastructure, facilities and equipment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.287
Teacher spread0.272 · 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 designNot applicable
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

Citations11
Published2016
Admission routes2
Has abstractyes

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