Innovative and promising practices in sustainable tourism
Bibliographic record
Abstract
The intent of this volume is to provide an opportunity for academics, extension professionals, industry stakeholders and community practitioners to reflect, discuss and share the innovative approaches that they have taken to develop sustainable tourism in a variety of different contexts. This volume includes nine cases from across North and Central America reaching from Hawaii in the west to New England in the east and from Quebec in the north to Costa Rica in the south. Case studies are a valuable way to synthesize and share lessons learned and they help to create new knowledge and enhanced applications in practice. There are two main audiences for this volume: 1) faculty and students in tourism related academic programs who will benefit from having access to current case studies that highlight how various stakeholders are approaching common issues, opportunities and trends in tourism, and 2) extension agents and practitioners who will gain important insights from the lessons learned in the current case study contexts.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".