Ecosystem sustainability and resource-based tourism : linkages and indicators
Bibliographic record
Abstract
The landscape of northern Ontario provides an ideal setting for resource-based \ntourism and, in recent years, the focus on tourism has increased due to the \npopularity of outdoor recreation and the notion that tourism can increase \ncommunity sustainability. Resource-based tourism is based on a wide range of \nactivities which are both consumptive and non-consumptive. As an industry, \ntourism can have significant impacts on natural, physical or social environments \nand it is important that the industry be managed sustainably. Currently, there is no \ngenerally accepted approach for examining the sustainability of the resource-based \ntourism industry and ensuring that resources are managed in the interests of future \ngenerations. The international forestry and tourism industries have adopted the \nconcept of sustainability indicators. Their initiatives provide guidance for the \ndevelopment of a regional framework for resource-based tourism. Through a \nworkshop and mail survey, members of the Northern Ontario Tourism Outfitters \nAssociation (NOTO) identified values that they believe are essential to the \nsustainability of resource-based tourism. This input, combined with data collected \nthrough a literature review, was utilized to develop a suite of indicators of \nsustainable resource-based tourism. An evaluation of each indicator was conducted \nand a revised framework of 23 indicators reflecting on ecological, economic and \nsocial values is presented. The framework will be useful to resource managers and \nthe tourism industry.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".