Kanata: Sustainable tourism in Canada? Lessons from the Covid-19 pandemic in a historic perspective
Bibliographic record
Abstract
The thesis will be centred on the topic of tourism in Canada and the steps the tourism industry took in handling the Covid-19 pandemic. The aim of this thesis will be to examine how Canadian tourism has evolved through time and analyse the way Indigenous tourism has grown despite its limited support. The main research questions addressed will include how has Canadian tourism adapted to the crises of the Covid-19 pandemic? How have the snowbird, Nature and Indigenous tourism groups been impacted by Covid-19 and how can we understand the history of these forms of tourism? What old and new debates were provoked by the Covid pandemic? What strategies were put in place to mitigate effects and how did historical structures of colonialism affect mitigation? The thesis is built using a series of academic texts, newspaper sources, and reports published by tourism and Indigenous tourism associations. The text will be approached through a historical perspective using the postcolonial theory and Adaptive co-management. The case studies analyses of Nature and Indigenous tourism are important as they represent tourism in Canada and illustrate the effects of colonialism stressing the need for reconciliation. The text will conclude with a look forward and a potential path tourism can take, learning from the Covid pandemic, to become more sustainable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".