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Record W4412805207 · doi:10.1080/08865655.2025.2539123

Border Wall Heritage Tourism: Attraction, Valuation, Significance

2025· article· en· W4412805207 on OpenAlexaffvenue
Victor Konrad

Bibliographic record

VenueJournal of Borderlands Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsCarleton University
Fundersnot available
KeywordsTourismValuation (finance)AttractionTourist attractionHeritage tourismContingent valuationEconomic geographyTourism geographyBusinessEconomicsPolitical scienceWillingness to payMicroeconomicsLawPhilosophy

Abstract

fetched live from OpenAlex

The substantial increase in walls and barriers worldwide calls for more extensive understanding of border walls and their tourist appeal. Border walls – materialized, appropriated, imagined – are important in our contemporary world because they manifest as an increasingly dominant statist response to global mobility and inequality. This article conveys the heritage dynamics of border walls as they emerge as geopolitically significant sites and landscapes, how these places become tourist attractions, how they are valuated, and how border wall heritage tourism manifests in overlapping regimes of heritage authenticity, touristic attraction, geopolitics, community, and institutional affiliation. Lenses of critical heritage, tourism, and border studies guide the valuation of border wall heritage through regional and global transformations, post-western understandings of culture, history and heritage, and the socio-political forces that actualize them. After providing a framework for valuation of border wall heritage tourism, this study examines cases in China, at the Berlin Wall and Cold War barriers in Europe, and the geomorphological walls of the Chile–Peru–Bolivia divide to offer insights about the shifting transnational, multiscalar, global and post-human dimensions of border wall heritage tourism.

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 score0.431
Threshold uncertainty score0.506

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.102
GPT teacher head0.324
Teacher spread0.222 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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