“My Jijii would always tell me: We're getting you ready. We're getting you ready”: Indigenous presencing in adventure tourism
Bibliographic record
Abstract
While there has been a general increase in Indigenous-owned and operated tourism companies across Turtle Island (currently known as North America), the same is not true for what is commonly referred to as “adventure tourism”, and specifically, in whitewater river canoe and raft guiding. There are still several barriers that continue to keep many prospective Indigenous guides out of the industry and off their traditional lands. This can work to perpetuate the myth that adventure tourism marketing relies on, that the rivers, lakes, and land they guide their clients on are an untouched and unpeopled “wilderness”. Peel River Watershed protector and activist Bobbi Rose Koe started an adventure tourism company, Dinjii Zhuh Adventures, to address these inequities. She aims to change the culture of guiding and tourism through introducing more Indigenous youth to their traditional watersheds, to train them as whitewater canoeing and rafting guides, and to support them in their employment and career development with the ultimate goal of land protection. This paper, co-written as a dialogue guided by the voice and perspective of Bobbi Rose, will weave together a story of the impact Indigenous presence has on the land, Indigenous youth, non-Indigenous guides and clients, other tourist operators, and the industry in general. Our conversation begins and ends with the role Indigenous presence plays in land and cultural governance and protection. We share these conversations, opportunities, challenges, and imagined futures to invite tourism researchers, operators, and guides to reflect, as well as to offer encouragement and camaraderie to other Indigenous guides and tourist operators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".