Bibliographic record
Abstract
Abstract Recent contributions in tourism economics acknowledge that the tourism market is imperfectly competitive and, as such, should be studied from an industrial organization perspective. This approach seems especially relevant to shed lights on one issue of importance for tourism destinations: how to achieve sustainable tourism development? Indeed, it has long been empirically observed that tourism development follows a life cycle. After a period of growth, the development of touristic (mountain and seaside) resorts usually stagnate and decline. At least part of the explanation for this pattern is to be found in the evolution of destinations' reputation over time. The present paper investigates the incentives for adjacent tourist resorts to invest in quality in order to maintain their collective reputation. We propose a dynamic model where (1) several adjacent tourist resorts select their tourist flows and (2) invest in order to remedy to the detrimental effects tourism flows have on local environmental amenities. The overall tourist presence and the sum of investments made by tourist resorts jointly define the quality of the touristic product offered by this tourism destination. We assume that this quality cannot be observed by consumers at the time of purchase. However, in this situation of imperfect information, consumers form expectations about the quality of the touristic product offered at any point of time. These expectations define the collective reputation of tourist resorts, determine the position of the tourist resorts' demand curve and constitute the state variable in the differential game. We characterize and compare equilibrium strategies under a noncooperative and investments coordination regimes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".