L'utilisation des indices de qualité dans les services : le cas des agences de voyage
Bibliographic record
Abstract
Une étude empirique menée auprès de 274 clients de deux agences de voyages a permis de dégager un ensemble d’éléments d’information privilégiés par ces clients dans leur évaluation du service reçu. Ces éléments d’information utilisés comme indices de qualité résident dans la satisfaction du client, sa perception des composantes de la «servuction» telles que l’image de l’entreprise, l’organisation interne, le support physique et le personnel en contact. Les résultats obtenus suggèrent au gestionnaire d’axer sa stratégie d’amélioration de la qualité de son offre de services sur, entre autres, la recherche de la satisfaction du client, l’introduction d’une «culture de qualité» au sein de l’entreprise et le positionnement de l’entreprise par son image de qualité supérieure.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".