MétaCan
Menu
Back to cohort

Environmentally Sustainable Lifestyle Indicators of Travelers and Expectations for Green Festivals: The Case of Canada

2019· article· en· W2943551521 on OpenAlexaboutno aff
Rachel Dodds, Philip R. Walsh, Burcu Koç

Bibliographic record

VenueEvent Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilitySustainable tourismAttendanceTourismMarketingPromotion (chess)BusinessRecreationSustainable developmentAdvertisingEconomic growthPolitical scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Festivals are emerging as one of the most attractive events in the tourism industry as their cultural and social wealth can contribute to the general promotion of a destination. Increased desire by communities to behave more responsibly has encouraged more sustainability-focused strategies on the part of festival organizers and have stimulated other industry stakeholders towards such actions. Accordingly, understanding what ecological behaviors might contribute to encouraging festival attendance can be important to planning a festival. In this regard, the main purpose of this research was to investigate real life environmentally sustainability tendencies of festivalgoers and their attitude towards attending environmentally sustainable festivals. Survey data were collected from 849 Canadian respondents who had attended a festival at least once in 2017. Our findings illustrate that a positive attitude towards attending a green festival is more strongly predicted by the level of intrinsic voluntary environmental actions that reflect personal commitment than by more mechanistic environmental activities such as waste reduction and recycling.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0090.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.213
Teacher spread0.210 · 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 designObservational
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

Citations14
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEvent ManagementSame topicEnvironmental Education and SustainabilityFrench-language works237,207