Contrived Landscapes: Simulated Environments as an Emerging Medium of Tourism Destinations
Bibliographic record
Abstract
This paper explores the idea of simulated tourism environments as an emergent medium of tourism destinations. Several simulated tourism environments will be exemplified from which some obvious characteristics of these artificial environments will be offered as a means of developing an understanding of this emerging phenomenon. Drawing on a range of sources including academic, popular fiction, and travel literature and promotional materials, the author(s) examine the way in which simulated tourism is developing. Although the discussion will focus mainly on simulated tourism environments, it will also address some of the philosophical considerations that underpin this visible trend. Subsequently, this paper will offer an analysis of how tourist experiences are affected as a result of the simulated settings. The analysis will include an examination of the differences between the motives of tourists involving themselves with these environments and those seeking more naturalized experiences. In conclusion, a brief handling of a select few global, commercial, and environmental trends will be presented with a view to present contradictions while posing scenarios and questions for future researchers concerning techno-enabled tourism products.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".