Bibliographic record
Abstract
Tourists make decisions that impact the places they visit. Through an economic and development perspective, tourism has grown into a capital venture for most countries all while having the challenging task of operating under specific policies that shape visiting experiences. These experiences are critical in assessing how, by and for whom land is developed and managed. This article explores three continents as case studies: Eastern Africa's Maasai Mara, Australia's Uluru-Kata Tuta site and the Torngat Mountains National Reserve Park in Canada. The African and Australian examples are based on participant-observation fieldwork by the authors while the Torngat Mountains serves as an example of what could become the new National Reserve Park in Canada and its possible tourism impact forecasting. Critical analysis is particularly important in this article as we examine, compare and contrast the development approach and land management policies from the tourist's experiential perspective. The purpose of this article is to illustrate the various levels and politics of planning involved in the recognition, nationalization and touristification of heritage sites as well as the creation of identities based on local confines. More specifically, with the focus on tourist experience, we attempt to uncover the nature of theory and practice in indigenous, private and public land management for tourism exploitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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.
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".