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Tourism and Cultural Heritage: Tradition, Innovation, and Global Challenges in the Media

2025· book-chapter· sr· W4414817346 on OpenAlexaff
Srđan Conić, Dušan Miljković

Bibliographic record

VenueЦентар за културу, едукацију и медије ``Академац'' eBooks · 2025
Typebook-chapter
Languagesr
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsNorthern Alberta Institute of Technology
Fundersnot available
KeywordsTourismGlobal challengesGlobalizationSocial mediaKey (lock)Tourism geography

Abstract

fetched live from OpenAlex

This paper explores the complex relationship between tourism and cultural heritage, with particular emphasis on the role of media in preserving, promoting, and innovatively interpreting cultural legacy in today's globalized world.It analyzes how traditional media and digital platforms influence the perception of cultural heritage, the formation of tourist routes, and the development of cultural identity.The applied methodology includes content analysis, case studies, and a comparative approach.The findings highlight the pivotal role of media in shaping cultural heritage narratives, while also pointing to risks of commercialization and loss of authenticity.The paper offers recommendations for an integrated approach to media, tourism, and cultural policy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.017
Scholarly communication0.0100.008
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.075
GPT teacher head0.246
Teacher spread0.171 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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