Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it