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Record W2155110209 · doi:10.1080/17400309.2014.880301

Film tourism as heritage tourism: Scotland, diaspora and<i>The Da Vinci Code</i>(2006)

2014· article· en· W2155110209 on OpenAlexaboutno aff
David Martin‐Jones

Bibliographic record

VenueNew Review of Film and Television Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
FundersScottish Government
KeywordsTourismDiasporaHeritage tourismChapelDepictionHistoryNational identityPaintingIdentity (music)Art historyMedia studiesArtVisual artsSociologyArchaeologyTourism geographyAestheticsLawPolitical scienceGender studiesPolitics

Abstract

fetched live from OpenAlex

Using the case study of The Da Vinci Code (2006), especially the extensive promotional activities surrounding the film (organised by VisitScotland, Maison de la France, and VisitBritain), this paper argues that film tourism be understood as a facet of heritage tourism. Scotland is a nation with a long history as a destination for heritage tourism, including literary and art tourism, whose brand identity in this regard functions slightly differently to that of England. Scotland has a large international diaspora, the result of its specific national history, which conceives of itself as Scottish, and returns – from New Zealand, Australia, Canada, and the USA – to events like Homecoming Scotland (2009) to reconnect with its roots in the manner of heritage tourism. The Da Vinci Code, like Braveheart before it and Brave since, appeals to this international audience through its depiction of Scotland. By analysing the film's construction of history, and the scenes shot in Rosslyn Chapel near Edinburgh (Rosslyn has previously featured in paintings and photographs, and was the star of a famous diorama in the early nineteenth century), it becomes evident that a sense of return and belonging is evoked in this Scottish setting that can resonate with heritage tourists.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.216
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.037
GPT teacher head0.322
Teacher spread0.285 · 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
GenreReview

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

Citations33
Published2014
Admission routes1
Has abstractyes

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