Analysis of Yukon's tourism industry post-pandemic: tourism labour market needs
Bibliographic record
Abstract
This study investigated the labour market needs faced by tourism businesses in Yukon, employing a convergent mixed-methods approach that integrated qualitative and quantitative data. The research identified a persistent mismatch between laboir demand and supply, consistent with national and global trends. Key factors contributing to workforce instability included seasonality, limited housing, competition from the public sector, and reliance on foreign and student labour. Additionally, skill shortages, both technical and interpersonal, were exacerbated by a lack of region-specific training opportunities. Recruitment strategies in Yukon largely relied on local networks and word-of-mouth, while retention efforts combined financial and non-financial incentives, reflecting community-oriented approaches. Government employment programs, though conceptually supportive, faced issues related to accessibility and alignment with the seasonal nature of tourism. Succession planning was marked by grow in case of eliminating or reducing barriers, including workforce limitations and housing challenges. The findings underscored the need for a multi-level intervention framework, guided by the Social-Ecological Model (SEM), addressing individual, community, institutional, and policy-level factors. This research offered actionable insights to enhance workforce sustainability in Yukon’s tourism sector.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".