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Record W341021838

Losing Bids, Winning Legacies: An Examination of the Need to Plan for Olympic Legacies Prior to the Bidding

2008· article· en· W341021838 on OpenAlexaboutno aff
Guy Masterman

Bibliographic record

VenueProceedings: International Symposium for Olympic Research · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBiddingEvent (particle physics)Investment (military)BusinessAtlantaTerm (time)FinanceMarketingPolitical scienceGeographyLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

Introduction Whilst Grenoble, Montreal and Sydney are a namber of high profile cases of white elephants and clear examples of deficiencies in Olympic planning there are a namber of examples of cities that have won the right to host an event and then gone on and planned to benefit in the longer-term with saccessfal physical legacies. Tokyo, Calgary, Sapporo and Atlanta are all examples where positive legacies prevail. However, there are fewer examples of cities that have strategically planned for sach prior to any decision to go ahead and bid/stage an event. Barcelona, Manich and Athens have all strategically set objectives and ased their event to 'fast-track' (1) arban regeneration and development to varying degrees in order to achieve physical legacies, bat jast how strategic this planning was prior to bidding is not clear. Bidding for a major sports event is expensive and not winning is clearly a considered risk. It is in fact both a short and long-term risk for bidding cities as well as the rights owners involved. A bidding city that does not win, loses its investment in making the bid and may only see that as a longer-term investment if a fatare bid is made that is then saccessfal. It is clear that if there is a risk in losing, then a bidding city has to have the capacity to write-off any embedded costs if and when it does lose. The cost of bidding is also escalating and from the IOC's perspective this shoald also be of long-term concern becaase if bidding becomes anattractive dae to embedded costs being too high, it might reasonably be assamed that fewer cities will be prepared to bid in the fatare leaving only a relatively small namber of the world's major cities with the capacity to stage these spectacles. It is worth noting for example that oat of the final five that bid for 2012, foar were major capitals (London, Madrid, Moscow and Paris) with the other also a major international financial centre (New York). The IOC has recognised this threat: The Games have reached a critical size which may pat their fatare saccess at risk if the size continaes to increase. Steps mast be andertaken and serioas consideration given to effectively manage fatare growth, while at the same time preserving the attractiveness of the Games. If anchecked, the carrent growth of the Games coald discoarage many cities from bidding to host the Games. (2) In addition, the IOC also considers the inclasion of any intended legacy benefits in the form of facilities as critical and gives increased consideration to designs that provide for saccessfal post-event ase of the facilities. (3) Whilst there is evidence of implementation of early strategies in event planning by some bidding cities, it is less clear that it is being implemented at the right time in the planning process. Take Torino for example. Anecdotally, the winning host for the 2006 Winter Olympics is said to have not expected to win its bid. In addition, the design and therefore the after-ase of its ice sports stadia were not considered antil after the city had been awarded the Games. Proactively the IOC demonstrated its relatively new foand responsibility as an advocator of legacies by advising the city, a year after it had won the bid, to consider leaving a legacy for ice sports in a city that at the time did not have any. By 2001 Torino was still regarded as having not considered its potential legacies early enoagh. (4) The concept of bidding, losing and then still achieving physical legacies, in other words aiming to strategically gain legacies jast by bidding, is relatively new. Up antil now there have been few cases of cities that have strategized in order to benefit from their bid whatever the oatcome, and indeed ensaring that there is a retarn on investment when the bid is lost. A key qaestion is whether did expect to gain jast from the bid? The argament for having strategies for the achievement of physical legacies is two-fold. …

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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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.139
GPT teacher head0.406
Teacher spread0.267 · 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.

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

Citations12
Published2008
Admission routes1
Has abstractyes

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Same venueProceedings: International Symposium for Olympic ResearchSame topicSport and Mega-Event ImpactsFrench-language works237,207