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Bounded Impacts: Measuring Residents’ Social (Media) Event Impacts From a Major Sport Event

2023· article· en· W4385517185 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.

Bibliographic record

VenueEvent Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of OttawaUniversity of Guelph
Fundersnot available
KeywordsEvent (particle physics)Social mediaDescriptive statisticsPopulationTourismPsychologySocial psychologyGeographyApplied psychologyDemographySociologyPolitical scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

This study measured if residents, or subsets of residents, experienced social event impacts (SEIs) and social media event impacts (SMEIs) from a major sport event. Panel data were collected from 1,027 individuals using an online survey 9 months postevent. Descriptive statistics indicated that although the event did not jeopardize residents’ safety or cause them conflict, it failed to produce positive SEIs and SMEIs, other than feel good factor, among the population. A cluster analysis revealed that while there was a subset of residents who experienced positive SEIs and SMEIs, over half were limitedly impacted, experiencing either no positive SEIs nor SMEIs or only SEIs. This research advances SEI-related theory by investigating impacts among all community members, not just attendees; measuring impacts further out from the event, not just shortly postevent; and introducing SMEIs. It challenges the notion that events elicit positive SEIs while identifying boundaries with respect to who experiences them.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.050
GPT teacher head0.329
Teacher spread0.278 · 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