Learn, Teach, Heal: Articulations of Indigeneity and Spirituality in Indigenous Tourism in British Columbia, Canada
Bibliographic record
Abstract
‘Learn, Teach, Heal’ encapsulates what seems to be occurring in Indigenous Tourism on Vancouver Island and the Haida Gwaii in British Columbia, Canada. Operating as a ‘Tourist-researcher’ in 2017 and 2018, I was there at a time when Indigenous Tourism was booming, partly facilitated by the political movement of Truth & Reconciliation. Tourism is often seen as a shallow, commercial and artificial activity, yet such a view risks speaking over the various reasons why hosts choose to engage in the industry. This dissertation offers a case study based on tours, performances and interviews with six people. The research foregrounds the voices and experiences of: Andy Everson, Tana Thomas, Roy Henry Vickers, Tsimka Martin, K’odi Nelson and Alix Goetzinger. In listening to how they present their work, I study how indigeneity and spirituality were being articulated in ways that relate to processes of decolonisation. Whilst they were all engaged in tourism for their own different reasons, a common theme that emerged was the goal to use tourism to learn, teach and heal, both for themselves and for their guests. Learning how to be guides and performers, their languages, traditional practices, histories and politics, they were able to explore with tourists aspects of their indigeneity and spirituality, illustrate diversity of peoples and practices, and teach about their values and hopes for the future. Healing is gained through having a space to learn and to teach, and to restore pride to the communities by taking control of the narratives. It is my contention that Indigenous Tourism is offering these six people sites of ‘becoming’ and ‘reclaiming’ in a way that puts decolonisation into practice.
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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 | 0.003 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".