Bibliographic record
Abstract
Purpose The purpose of this paper is to build on the concept of using a population or portfolio of events to help rejuvenate or redefine the strategic position of a destination. The aim is to gain a general understanding of the local community outlook towards a process of repositioning the tourism product based on a portfolio of sporting events. Design/methodology/approach A quantitative research design using a case study approach examined resident attitudes in a beach community of south Italy. In total, 740 questionnaires were received and a cluster analysis was used to study the 11 statements about residents’ perceptions of tourism development and sport events. Findings The findings reveal that resident attitudes towards tourism development are strongly related to their perceptions of their degree of involvement in the setting of strategy and the direction of development. The results also support previous beliefs about increasing interest in the sport tourism product and that sporting events are viewed as important drivers of tourism destination development. The research reveals the presence of different resident attitudes and the cluster analysis is helpful in finding homogeneous groups of residents within the destination. Originality/value There is limited understanding of the degree to which the local community fits into the plans of a city's pro‐growth agenda and the role that a tourism strategy based on sport events can have. This is particularly true in southern Italy where the classical sun, sea and sand (3S) tourism model is in severe crisis and new ways of development are urgently required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".