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Record W2094131028 · doi:10.1080/14766825.2014.939392

Branding Copán: valuing cultural distinction in an archaeological tourism destination

2014· article· en· W2094131028 on OpenAlexaff
Lena Mortensen

Bibliographic record

VenueJournal of Tourism and Cultural Change · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
Fundersnot available
KeywordsTourismEconomic rentValue (mathematics)PhenomenonCultural heritageEconomyTourism geographyHeritage tourismSociologyPolitical scienceEconomicsMarket economyLawEpistemology

Abstract

fetched live from OpenAlex

In this article I explore the phenomenon of heritage tourism branding in the country of Honduras, focusing on the archaeological site of Copán, the country's highest profile cultural destination. Branding operates as a primary mode through which tourism value is presumed to circulate and reproduce, and become available for exclusionary regimes to capture new economic rents. As a heightened mechanism for mobilizing symbolic distinction, branding now also appears to organize value well beyond the domain of economic exchange. Branding the past takes on particular resonance in Honduras, where a claim on the ancient Maya, materialized through the Copán Archaeological Park, sustains a significant sector of the tourism market upon which a regional economy now depends. As in other areas of place-based and culture-based tourism, branding practices and logics increasingly dominate the organization of tourism encounters at Copán in both explicit and implicit ways. I use the Honduran case to focus discussion on the many different dimensions of branding that inhere in heritage sites, and the consequences for how such complexes of value are negotiated beyond the market.

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)0.001
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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.098
GPT teacher head0.375
Teacher spread0.277 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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