Asset mapping for sustainable tourism development in UNESCO’s Frontenac Arch Biosphere reserve
Bibliographic record
Abstract
Tourism is an evolving sustainable development pathway for rural communities, which UNESCO biosphere reserves are well-positioned to contribute to. The potential benefits of rural tourism, however, have not always taken shape as predicted, or are sometimes distributed inequitably. Responding to a need for more strategic sustainable tourism development strategies that generate livelihood for rural communities, we conducted a geographic asset mapping case study in the Frontenac Arch Biosphere (FAB), Ontario, Canada. Working with community partners, this research aimed to identify and, where relevant, map the tangible and intangible assets that may support sustainable tourism development in the FAB and understand the challenges impeding these developments across the Biosphere’s three distinct zones. Through asset mapping workshops and interviews involving tourism operators, artisans, farmers, and conservationists, spatial and thematic findings were summarized and interpreted using a capitals framework, comprising seven types of capital identified in the literature as central to sustainable rural development and livelihoods. 128 tangible assets were mapped and a series of intangible assets were identified, including an ethic of sustainability and the capacity to teach, among others. These findings provide insights into how local assets, tangible and intangible, can be leveraged in a coordinated way to facilitate sustainable 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".