Interpretation and Total Experience Management (TEM) as innovative methods for sustainable nature based tourism : a benchmarking analysis
Bibliographic record
Abstract
In the trans-modern society tourism has to transform into meaningful and learning experiences that contribute to a better and sustainable world. Integrating the methodology of natural interpretation and environmental education with developing theories of meaningful experience production, new innovative nature tourism products can be developed for glocal sustainability. Combining Marslow´s pyramid of needs with TQM, I developed the concept of Total Experience Management (TEM), as a powerful tool for qualitative experience production. Combining interpretation and TEM, innovative nature experiences with meaningful bearings on sustainability can be produced according to Pine & Gilmore´s model of the four experience realms of entertainment, education, escapism and aesthetics. Based on the methods of interpretation and TEM I have developed a benchmarking tool which I tested on 15 nature and cultural based guided tours in Canada, Australia, Finland, Norway, Iceland and Sweden. Within two projects for developing guiding qualities we surveyed 115 entrepreneurs and tourism organisations in Finland, Sweden and Norway, about their view on "guide competence" and quality certification of guides. The benchmark study showed that interpretation as method is still rare and there are needs for quality improvements of the nature experience production in the light of TEM. The business survey indicated a need for certification and quality improvement of nature guides. However, when hiring a guide their education and reputation was more important then their certification. I thus conclude that the methods and skills of the nature guide could be a key factor for improving sustainable outcome of nature based tourism.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".