The Tourism Adaptation Classification (TAC) framework: An application to New Zealand's Glacier country
Bibliographic record
Abstract
Alongside growing awareness of the significance of environmental change for glacier regions, and their tourism-dependent communities, is the realization of the need to adapt to changing conditions. Such adaptation is necessary for tourism operators, managers, and planners as well as the visitors themselves, and is part of building resilient tourism systems. This paper presents a conceptual framework for understanding the possible stages of adaptation in glacier tourism destinations. The Tourism Adaptation Classification (TAC) framework aligns three stages of adaptation (resilience, transition, and transformation) against adaptation strategies implemented by tourism stakeholders and identifies specific characteristics. Using a desk-based case study approach, the framework is illustrated with reference to Glacier Country in New Zealand's Westland/Tai Poutini National Park in relation to three core dimensions of the tourism system: tourism planning and governance; tourism business and operations; and visitor experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".