Seasoned travelers are more sustainable: modelling the tourism experience life cycle
Bibliographic record
Abstract
Mapping a tourist’s travel frequency and behaviour over time (as outlined in the Tourism Experience Life Cycle (TELC)), may be as important as Butler’s Tourism Area Life Cycle (TALC) as there is a ‘need to understand the life cycle for a tourist’ (Dodds, 2020, p. 219). This paper, using a quantitative approach of 980 Canadians, tests the validity of the TELC model to determine if sustainable travel behaviours increase the more a tourist travels. Two hypothesis are tested in this paper. First, the more trips taken by a traveller, the more sustainable their behaviur will be and second, the more a traveller revisits the same destination, the more sustainable their travel behaviour will be. Findings, supported through ANOVA and hierarchical multi-step regression, show that there is a relationship between the number of trips taken and sustainable behaviour. The greater the number of domestic and/or international trips that a leisure traveller takes, the more likely their behaviour while travelling will be more sustainable. On the other hand, findings also outline that the more frequently a visitor returns to the same destination, the less likely they will practice sustainable travel behaviour.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".