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Record W3071925162 · doi:10.1108/ijefm-03-2020-0012

Shaping our perception of reality: sustainability communication by Canadian festivals

2020· article· en· W3071925162 on OpenAlexaffabout
Rachel Dodds, Michelle Novotny, Sylvie Harper

Bibliographic record

VenueInternational Journal of Event and Festival Management · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSustainabilityPublic relationsContent analysisValue (mathematics)Best practiceCategorizationPerceptionMarketingSustainability organizationsSociologyBusinessPolitical sciencePsychologySocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to determine the extent of online communication by festivals regarding their sustainability practices using Cultivation Theory as the framework to determine perceived value creation. Design/methodology/approach A mixed method approach was utilized to achieve data triangulation through a content analysis of websites, content analysis of social media sites as well as interviews. Findings Findings indicated that 64% of festivals did not communicate any sustainable practices through their websites and only 6% communicated via social media. The most common sustainability practices communicated were waste management and sustainable transportation, yet few festivals engaged in effective, consistent and sufficient marketing of initiatives to festivalgoers. Best practice festivals (having communicated 5.47 initiatives or more) were found to have been significantly more likely than non-best practice festivals to be music festivals and have been in operation longer. Best practice festivals were also more likely than non-best practice festivals to have sustainability engrained into their corporate philosophy via a communicated sustainable vision and mission. Interviews revealed that most festivals did not have a designated role responsible for all sustainable initiatives and the responsibility was often taken on by volunteers or festival organizers. Festival organizers that communicated sustainability initiatives efficiently, consistently, and sufficiently perceived these efforts to benefit the festivals value amongst festivalgoers and host communities. Propensity to communicate sustainability initiatives was found to have been impacted by awareness, categorization, timing, policy and funding. Research limitations/implications While the findings are limited to the country of Canada and the extent of communication on websites and social media platforms as well as those festivals who participated, interviews helped to overcome these limitations as they gained an understanding of what was undertaken but not necessarily communicated. Practical implications The findings generated from this study could be used as a guide for establishing a benchmark for festivals regarding sustainable communication as well as strategies for overall corporate responsibility. Content regarding sustainability at festivals is scarce, as is information on festival communication. As a result, this paper seeks to understand the sustainable initiatives that are being communicated by festivals. Originality/value This is the first time Cultivation Theory was used within a tourism context and may be a useful tool to determine value creation. Through Cultivation Theory, festival organizers believed to have the ability to impact perceived value of the festival by implementing efficient, consistent and sufficient communication of sustainability initiatives.

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)0.005
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0190.008
Scholarly communication0.0100.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.055
GPT teacher head0.377
Teacher spread0.321 · 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

Citations30
Published2020
Admission routes2
Has abstractyes

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