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Record W1605808271 · doi:10.1080/02508281.2015.1049814

A sense of place: place, culture and tourism

2015· article· en· W1605808271 on OpenAlexaffabout
Stephen L. Smith

Bibliographic record

VenueTourism Recreation Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTourismTerroirSense of placeSociologyTourism geographyCultural tourismVariety (cybernetics)StorytellingContext (archaeology)NarrativeAestheticsSocial sciencePolitical scienceWineHistoryVisual artsArt

Abstract

fetched live from OpenAlex

The perspective of ‘place’ has emerged as important one in many realms of scholarship and professional practice. Ironically, though, tourism – in which place is a central concept – has paid little attention to the concept. This paper explores the importance of place in the context of tourism product development and marketing, particularly the linkage between place and culture as a tourism experience. The linkage can be understood through an analogy to the concept of terroir from viniculture. Terroir is the set of qualities that shape the sensory and intellectual appreciation of a wine, including soil, climate, grape variety and wine-making techniques. In the case of placed-based cultural tourism development and promotion, the terroir of a place includes history, local traditions and cultures, religion, industry, the natural environment, cuisine and arts, as well as attractions and events. A key feature of place-based product development and promotion is the identification and telling the story of a place through a variety of narrative techniques. These techniques include the tradition of oral storytelling, but also print, video, graphic and digital media. The paper examines the themes of stories often told in connection with place-based cultural tourism and illustrates these with examples drawn from a Canadian cultural destination.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.109
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.049
Scholarly communication0.0130.007
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.112
GPT teacher head0.424
Teacher spread0.312 · 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

Citations151
Published2015
Admission routes2
Has abstractyes

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