Sustainable Tourism Indicators: Selection Criteria for Policy Implementation and Scientific Recognition
Bibliographic record
Abstract
Using sustainable tourism indicators (STI) creates many difficulties resulting mainly from the multiple interpretations of the concept of sustainable development, and by extension of the concept of sustainable tourism. To these difficulties are added an absence of a strong academic background, which is the result of incompatibilities between the needs and objectives of the academic versus the political world, which often challenges the need for indicators. We propose a parsimonious list of sustainable tourism indicators based on the application of a series of selection criteria. From the expert recognized indicators, all of these criteria help us choose the indicators, which cover the dimensions and issues of sustainable development for tourism. They are legitimized by existing experiences and sufficiently flexible to be useful for different destinations. In the end, the intersection of these conditions contributes to the scientific and political recognition of the indicators. We start by applying four general selection criteria to a 507 STI database. This allows us to reduce the list to 20 recognized STI. We end the selection process by applying three specific criteria in order to adjust the 20 STI to render them operational. We illustrate the selection procedure with an example of criteria application to the Gaspésie-Iles-de-la Madeleine region in Quebec. L'utilisation d'indicateurs de tourisme durable (STI) pose de nombreux problèmes qui résultent principalement des multiples interprétations du développement durable et, de ce fait, du tourisme durable. S'y ajoute l'absence d'un cadre de référence établi résultant de l'incompatibilité entre les attentes et objectifs du milieu académique et du milieu politique et remettant souvent en cause la crédibilité et le bien-fondé des indicateurs. Pour y remédier, nous proposons une liste parcimonieuse d'indicateurs de tourisme durable (STI) basée sur l'application d'une série de critères de sélection. L'ensemble de ces critères permet de choisir, parmi les indicateurs reconnus par les experts, ceux qui couvrent largement les dimensions et les enjeux de développement durable dans le domaine du tourisme, qui sont légitimés par les expériences existantes et qui sont en même temps suffisamment flexibles pour être effectifs et utiles à différentes destinations. Nous croyons que le concours de ces conditions contribuera à la reconnaissance et à la légitimité scientifique et politique des indicateurs. Quatre critères de sélection généraux sont appliqués à une base de données de 507 STI pour en réduire le nombre à un effectif optimal de 20 STI. Ensuite, trois critères spécifiques permettent d'ajuster les 20 STI pour les rendre opérationnels. Nous illustrons cette démarche en appliquant ces critères à la région de la Gaspésie-Îles-de-la-Madeleine, Québec.
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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 | 0.056 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.031 | 0.036 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".