Des technologies et des destinations touristiques intelligentes : entre rhétorique et expérimentation
Bibliographic record
Abstract
Réseaux intelligents, destinations intelligentes, villes intelligentes…. sont des concepts qui ont le vent en poupe ces dernières années. Ils sont les composantes d’une rhétorique qui prônent des idéaux technos/éco systémique à travers le déploiement des technologies dites « intelligentes », de la gestion optimisée de l’énergie, pour in fine prétendre participer à l’amélioration de la qualité de vie des résidents et des touristes. Cette contribution a pour objet de mettre en perspective les transformations que connaissent les villes et les destinations touristiques à l’ère des transformations technologiques et managériales. Elle tente de proposer un regard croisé de la convergence entre le tourisme et les technologies d’information dans le cadre de la destination intelligente. Dans la seconde partie, nous présenterons succinctement les travaux sélectionnés pour ce numéro spécial de la revue Études Caribéennes.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".