Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".