Tourist Carrying Capacity Measures: Crowding Syndrome in the Caribbean
Bibliographic record
Abstract
International tourism is increasing at an unprecedented rate. Understanding the variety of national and local impacts of this increase is of importance to a growing number of governments. CitationButler's resort cycle model (1974, Citation1980,; Citation1991) provides for several long-term possibilities as to the relationship between crowding and growth. CitationMcElroy, de Albuquerque, and Dioguardi (1993) focus on one of those possibilities. Specifically, using their penetration ratio, they predict that as tourist crowding continues for a group of Caribbean islands, the appeal of these islands decreases in the eyes of potential tourists and that, as time increases, the growth rate of the affected islands, actually decreases. Our article indicates that such a simple, straight-line relationship between increased crowding and a decrease in the rate of change may not be inevitable; indeed, diseconomies of scale may be avoided. The use of a curvilinear regression function reveals how both positive and negative scale economies existed in the Caribbean during the years 1992 through 1996.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".