Sustainable tourism indicators: selection criteria for policy implementation and scientific recognition
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
The use of sustainable tourism indicators (STI) raises several issues, mainly because of the multiple interpretations of the concept of sustainable development, and by extension of the concept of sustainable tourism. It also brings to light incompatibilities between the needs and objectives of academics and policy-makers in developing a set of STI. The STI are then either scientifically relevant but too complex to be operational, or else they result from a political consensus, which could lead to conflicts of interest, such as in the destination branding strategy. In this paper, we argue that the trade-off between academic and policy-maker approaches to indicator development can be achieved through the development of core STI, based on the application of two sets of selection criteria to 507 expert-recognized indicators. The first set of criteria allows us to select 20 core STI, while the second set of criteria aims to match the selected indicators with a destination's policy framework in order to guarantee their usability. We illustrate the selection procedure using the Gaspésie region in Québec as a case study.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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