Identifying barriers to environmentally sustainable tourism: Exploratory findings from the Bighorn Backcountry
Bibliographic record
Abstract
Recent interest in actively developing the tourism industry in the Bighorn Backcountry of Alberta has caused concern over the sustainability of such development. There is agreement among tourists, developers, and other stakeholders that the authentic environment, free of large amounts of infrastructure, should be preserved. To maintain a sustainable tourism environment without hard infrastructure mitigating environmental impact, the Alberta Government has identified a target tourist type, authentic tourists. Authentic tourists are considered to have a high level of ecological concern for the destinations that they visit, resulting in the province’s expectation that such tourists will exhibit a high level of responsibility for environmental sustainability. I interviewed tourists staying in the Bighorn Backcountry, representative of the authentic tourist type, in order to explore challenges related to the proposed development model. I identified a conflict between how tourists perceived their responsibility for the environmental sustainability of their destination and the expectation for responsibility that the development plan is reliant on. Use of online platforms is explored as an approach for overcoming the identified conflict. Findings suggest there is potential in the anticipation stage of travel to engage tourists with environmental concerns and initiatives. Requirements for information to be accessed and utilized by tourists include the need for convenience, trustworthiness, and presentation of facts rather than opinion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".