BEST EN Think Tank VII Innovations for Sustainable Tourism The Community of Communicators and the Communication of Sustainable Development Management to Visitors of a National Park
Bibliographic record
Abstract
In spite of the trend towards business as a key element in society and tourism, governments still play an important role in the sustainable development debate. Like any social institution, governments and related organizations do not always function effectively. When they do not, for some reason, we (academics, business people, government officials and the general public) often argue that the solution is to impose a “made in business ” solution. However, given the different goals of government and the business sector, that approach may not be appropriate or necessarily successful. This paper develops a “made in government ” solution to a public institution that was not functioning well. The solution had direct impact on the sustainable development of a major national park and international tourist destination – Banff National Park, Canada. It was a creative solution that also addressed a sustainable development issue; that of providing education to people in order to develop their knowledge of the sustainable development management of natural resources. Richard Sharpley (2000) identified one of the requirements for sustainable development to be “the adoption of a new social paradigm relevant to sustainable living ” (Sharpley, 2000, p. 13), which necessitates learning and hence communication.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.031 |
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".