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Record W4415586114 · doi:10.21083/crrf.v31i1.7341

Marketing and a Million-acre Farm: Using Culture and Islandness to Promote PEI as a Unique Tourism Destination

2023· article· W4415586114 on OpenAlexfundno aff
John Campbell

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2023
Typearticle
Language
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersInnovation, Science and Economic Development Canada
KeywordsTourismGlobeDestinationsLeverage (statistics)Tourism geographyTourist destinationsProduct (mathematics)

Abstract

fetched live from OpenAlex

Islands across the globe are enjoying tourism growth, accounting for a far greater percentage of the tourism market than their size would suggest, and collectively becoming the world’s second most popular tourist destination. Culture is an essential and fast-growing component of tourism, used by destinations to broaden their product and showcase their uniqueness. Research has found that small islands in particular can leverage culture to showcase their local identity. With 1.56 M tourists and receipts of $447 M annually, tourism is Prince Edward Island’s second-largest industry, accounting for 6.4% of its GDP and contributing handsomely to the province’s sustainability. Through Tourism PEI, its marketing arm, the Island has long employed its culture and “islandness” as a magnet for tourists. Through a review of academic and grey literature, combined with interviews of tourism marketing officials, this study explores and compares the extent to which the Island’s regions and municipalities use culture in their marketing materials and assesses how tourism players define “islandness” and use it as a distinguishing feature to attract visitors.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.024
GPT teacher head0.311
Teacher spread0.287 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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