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Vanishing Peripheries: Does Tourism Consume Places?

2013· article· en· W2079302371 on OpenAlexaff
C. Michael Hall, David Harrison, David Weaver, Geoffrey Wall

Bibliographic record

VenueTourism Recreation Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTourismContext (archaeology)Consumption (sociology)Experiential learningSociologyGovernment (linguistics)Economic geographyPolitical scienceGeographySocial scienceLaw

Abstract

fetched live from OpenAlex

ContextThe notion of the periphery and its relationship to tourism is one that has been a source of debate for many years. The concept of a periphery obviously raises the question of peripheral to what, while the term is often used in a relatively negative context with respect to levels of development and/or influence on central government decision-making. What the term does raise of course is the extent to which location still matters at a time when physical, virtual, capital and human mobility is supposedly greater than ever before. The four contributions therefore highlight a number of key points and debates surround the relative importance or, and relationship between, location and tourist movement.The lead piece by C. Michael Hall, from the Pacific periphery of the South Island of New Zealand, is charged with the topic of does tourism consume place and therefore lead to the loss of the periphery, and perhaps some of the very qualities that attracted tourists to it in the first place. The first response by David Harrison, also from the Pacific, looks at both the geographical and broader sodal scientific understandings of periphery. The second response from David Weaver utilizes the concept of experiential consumption to interrogate Hall's paper and also link to Harrison's reference to the importance of development theory in understanding notions of periphery. The final response from GeoffW all approaches the topic from an overtly geographical perspective and brings the research probe full circle.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.019
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.070
GPT teacher head0.402
Teacher spread0.332 · 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

Citations46
Published2013
Admission routes1
Has abstractyes

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