Tourism, ecotourism and sport tourism: the framework for certification
Bibliographic record
Abstract
Purpose – The purpose of this paper is to develop a framework for identifying the need for ecotourism certification within ecotourism and sport tourism (EST) by discussing the overlapping characteristics on the dimensions of EST. Design/methodology/approach – Qualitatively, the Social Exchange Theory was used to discover segments of tourists based on the two dimensions: EST. Findings – The findings discovered four strategic segments (namely; vacation, green, action oriented and active tourists), their related activities, and the level of need for eco certification. Practical implications – EST activities offer a unique opportunity for tourism managers to positively influence conservation in and around communities, protected areas and sport events. Applying and implementing a global eco certification is paramount to attract tourists and enhance credibility of sport tourism. Originality/value – Identification of the four tourists segments and their relative need for certification is the novelty of the study. The labels of the identified tourist segments are: vacation tourist (low on ecotourism and low on sport tourism); green tourist (high on ecotourism and low on sport tourism); action-oriented tourist (high on ecotourism and high on sport tourism); and active tourist (low on ecotourism and high on sport tourism). The certification needs for green and action-oriented tourists are HIGH, for active tourist is MEDIUM, and for vacation tourist is LOW.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".