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Tasting Place: Themes in Food and Beverage Product Logos from Three North Atlantic Island Regions

2020· article· en· W3013636652 on OpenAlexaboutno aff
Maggie J. Whitten Henry

Bibliographic record

VenueGastronomy and Tourism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsLogos Bible SoftwareTourismWine tastingLogo (programming language)Place brandingProduct (mathematics)DestinationsAdvertisingPromotion (chess)MarketingThematic analysisFoodwaysGeographyBusinessSociologyPolitical scienceQualitative researchArtSocial scienceWineVisual arts

Abstract

fetched live from OpenAlex

Islands have long been romanticized for their potential to facilitate the kind of escape from globalization increasingly sought by neolocalism-driven consumers, and are thus uniquely positioned to emphasize their distinctive environment and culture through a holistic destination brand that targets both the tourism and local product markets. The current study examines the relationship between destination brands and local food and beverage brands in three North Atlantic island regions: Newfoundland, Iceland, and Shetland. Using a blend of content and thematic analysis to identify and analyze prominent themes employed in product logos, this study offers insight regarding food and beverage branding approaches in island contexts and their relationship to regional destination brands. Throughout the content examined for this study, island-based food and beverage producers demonstrated an intense and dynamic connection to place, as exemplified through the themes of place, culture, and environment embedded in their logos. Discussion of the study findings highlights the importance of strong logo branding for entrepreneurial success and regional tourism promotion, and advocates for future research and practical implementation of effective branding and logo design.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.998

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 head0.029
GPT teacher head0.251
Teacher spread0.222 · 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 teacher head, 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

Citations2
Published2020
Admission routes1
Has abstractyes

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