An asset analysis of the Yukon Territory for sustainable tourism development
Bibliographic record
Abstract
Purpose: This research represents the first comprehensive post-pandemic analysis of tourism assets across Yukon Territory, undertaken in partnership with the Tourism Industry Association of the Yukon (TIAY) to address critical knowledge gaps regarding the current state, availability, and diversity of tourism-related infrastructure. These gaps include: (1) the actual number and distribution of tourism businesses across communities, (2) the operational status and recovery patterns of businesses post-pandemic, (3) the degree of tourism service diversification in rural communities, and (4) the systemic barriers preventing tourism development despite natural and cultural assets. These knowledge gaps are critical because they impact evidence-based policy development, efficient resource allocation, and strategic investment decisions in a territory where tourism represents a primary economic driver. The study also examines how northern destinations like Yukon can develop sustainable tourism systems while preserving their unique cultural and ecological integrity, responding to the intersection of environmental vulnerability, infrastructure limitations, and pandemic-related disruptions that have created challenges for territorial tourism development. Methodology: The research employed a mixed-methods approach using the asset database, quantitative survey analysis, and qualitative interviews of tourism business owners/managers. The asset inventory identified 590 tourism businesses across 21 communities and 11 sectors—significantly exceeding the 400 businesses previously estimated for planning purposes. Data collection included an online survey distributed to all identified businesses (90 completed responses), semi-structured phone interviews with tourism operators, and field observations conducted during a familiarization tour. Analysis was conducted using SPSS, Microsoft Excel, and NVivo to integrate quantitative patterns with qualitative insights from industry stakeholders. Results: Key findings address critical knowledge gaps: (1) Tourism asset distribution follows extreme concentration patterns, with 51.9% of businesses in Whitehorse and 81.4% along highway corridors, revealing that infrastructure determines rather than supports tourism development; (2) Two communities (Whitehorse and Dawson City) achieved complete tourism service diversification (3) Seasonal operations create a 59% average staffing reduction, representing not just demand variation but systemic operational discontinuity that existing tourism theory does not address; (4) Housing emerged as a cascade constraint, simultaneously limiting workforce availability, business expansion capacity, and visitor accommodation; (5) Post-pandemic assessment shows 62% of businesses pursuing growth strategies despite these constraints, suggesting resilience mechanisms not predicted by conventional crisis recovery models. These findings reveal that the critical knowledge gaps were not simply about counting businesses, but understanding how infrastructure dependencies create tourism development paradigms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".