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Examining Long-term Organizational Forms Surrounding Leverage and Legacy Delivery Of Canadian Major Sport Events

2022· article· en· W4205941497 on OpenAlexaffabout
Kylie Wasser, Landy Di Lu, Laura Misener

Bibliographic record

VenueEvent Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsLeverage (statistics)Public relationsConceptual frameworkBusinessEvent (particle physics)Knowledge managementMarketingPolitical scienceSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This study explored the long-term organizational forms that are responsible for leverage and legacy delivery of major sport events. Comparative cases from the 2010 Vancouver Olympic Winter Games and the 2015 Toronto Pan Am/Parapan American Games were used to examine what mechanisms previous host cities have used. The findings from this study demonstrated that important organizational mechanisms contributing to strategic leveraging efforts included frequent collaboration from earliest point; distinction from the OC; the use of binding policy to maintain partnerships; clearly defined roles, responsibilities, and guidelines of conduct; and consistent, sustainable leadership. The collaborative nature of these organizational forms provided opportunities for organizations to increase their leveraging capacity. A conceptual framework for leveraging sustainable outcomes within the complex and multilayered nature of collaboration is also developed as a starting point for organizers looking to deliver lasting impacts from an event, as well as for scholars examining event legacy and/or leveraging strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0110.005
Scholarly communication0.0070.003
Open science0.0020.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.033
GPT teacher head0.270
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2022
Admission routes2
Has abstractyes

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