Thoughts from the think tank: lessons learned from the sustainable Indigenous tourism symposium
Bibliographic record
Abstract
Indigenous tourism has the ability to foster, promote and preserve Indigenous culture and traditions and is closely in line with Indigenous perspectives. In Canada, Indigenous tourism has been developing at an exponential rate. This paper is a synopsis of a think tank held in Nanaimo, British Columbia at the Sustainable Indigenous Tourism Symposium in April 2017. The purpose of the symposium was to create an interactive forum to facilitate the sharing of knowledge and support community empowerment, cultural expression and economic prosperity within the tourism industry for Indigenous communities in Canada and globally. On the final day of the symposium, a think tank was held to identify key challenges and issues facing the development and competitiveness of sustainable Indigenous tourism in Canada; to share innovations in sustainable Indigenous tourism amongst all stakeholders; and to identify how to support stakeholders in Canada and abroad with the knowledge and tools to develop sustainable Indigenous tourism. The think tank included consultation and collaboration with 82 stakeholders including Indigenous peoples, students, academics, government actors, tour operators and tourism associations. The results of the think tank identified several key themes and the Naut’sa mawt Declaration of the Development of Sustainable Indigenous Tourism was developed from these discussions.
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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 | 0.050 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".