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Visiting Friends and Relatives Travel: Unveiling Hidden Drivers Behind Festival Attendance and Experience

2024· article· en· W4391782591 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 · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAttendanceAdvertisingTourismMusic festivalMarketingPsychologyGeographyBusinessVisual artsArtEconomic growthEconomics

Abstract

fetched live from OpenAlex

This research note unveils a pivotal, yet underexplored, aspect of festival attendance: the impact of visiting friends and relatives (VFR) travel. Employing data from a 2019 attendee survey at the Taste of Little Italy Festival, Toronto, it reveals that 23.3% of respondents were engaged in VFR travel, exhibiting higher spending, yet often providing lower evaluations of their festival experience. The nuanced relationship between VFR travel, spending patterns, and festival experience opens a new avenue for exploration for festival researchers and practitioners. This note aims to encourage festival researchers and practitioners to consider the implications of VFR. A more comprehensive understanding of this topic could reveal strategies to engage this stable demand source, influencing not only event management strategies but also enhancing cultural engagement and community attachment. The note underscores the opportunity for festivals to engage residents and their visitors to optimize both economic and experiential outcomes.

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)0.000
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 score0.519
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.334
Teacher spread0.316 · 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