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Record W2338285154 · doi:10.3167/nc.2016.110102

Nature, History, and Culture as Tourism Attractors: The Double Translation of Insider and Outsider Media

2016· article· en· W2338285154 on OpenAlexaffabout
Mark C. J. Stoddart, Paula Graham

Bibliographic record

VenueNature and Culture · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTourismNewspaperSociologyInsiderSocial mediaMass mediaDigital mediaGovernment (linguistics)Political scienceMedia studiesAdvertisingBusiness

Abstract

fetched live from OpenAlex

Since the 1992 cod fishing moratorium, the province of Newfoundland and Labrador has redefined social-environmental relationships with coastal landscapes in pursuit of tourism development. We explore how coastal landscapes are defi ned for tourists through traditional and digital media produced by Newfoundland-based tourism operators and the provincial government. We examine how these discourses are then translated by "outsider" mass media in Canada, the US, and the UK, thereby connecting local environments to global flows of tourism. To understand this process of translation and circulation we analyze television ads, websites, and newspaper articles. Additional insight is provided through interviews with tourism operators and promoters about their media work. Drawing on a co-constructionist approach and tourism mobilities literature, we argue that the post-moratorium shift toward tourism has resulted in the packaging and insertion of Newfoundland landscapes into global tourist/travel discourses in multiple ways that depend on medium of circulation and target audience.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.754

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.028
GPT teacher head0.319
Teacher spread0.291 · 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 designNot applicable
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

Citations11
Published2016
Admission routes2
Has abstractyes

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