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"We … We Had Fun, We Did Have Fun": Long-term Sport Event Outcomes and Community Tensions

2022· article· en· W4293014364 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEvent Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEvent (particle physics)Context (archaeology)Public relationsGovernment (linguistics)TourismMarketingAffect (linguistics)Political sciencePsychologyBusinessGeography

Abstract

fetched live from OpenAlex

Publicly funded sport events (may) affect the relationship between governments and residents. The use of taxpayers' money creates certain expectations, including community-focused event outcomes. The purpose of this article is to investigate the alignment of event objectives and outcomes between host residents and those responsible for bringing a publicly funded sport event to a city, namely the government. The 2011 and 2019 Canada Winter Games, two publicly funded, non-mega-, multisport events, provided the context for the study. Data were collected through documents, interviews, and focus groups. Our findings are articulated in three themes, highlighting that although residents often evaluated their respective events positively, event experiences diverged from those of event providers. Our findings support the need for multisectoral event portfolios to pursue community objectives and public engagement strategies throughout the event planning process. These results may help event providers meet the needs of host residents when hosting future sport events.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.346
Teacher spread0.286 · 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