Understanding Motor Vehicle-Based Travel: Examining the Experiences of Yukon Tourists
Bibliographic record
Abstract
In the past several decades, scholarly research has simultaneously expanded in three research areas: northern tourism, drive tourism and the tourist experience. This study used an exploratory approach to understand the relationship between those three areas through a case study of the Yukon. Lead by four guiding questions 1) what are motor vehicle-based tourists’ expectations of Yukon? 2) how do Yukon tourists’ expectations influence their motor vehicle travel? 3) how does the motor vehicle influence tourists’ experience in the Yukon? and 4) how is Yukon reflected in the narratives of motor vehicle-based tourists? a mixed methods approach was used to collect data, and both qualitative and quantitative techniques were used to analyze the results. Thirty-nine participants completed semi-structured questionnaires on-site and in-person. Through a combination of content analysis and descriptive statistics, this study answered the guiding questions using the quantitative results and the themes and categories that were derived from tourists’ narratives. Participants from this study were largely repeat visitors to Yukon who started their trip from within Canada seeking nature-based experiences as much as they sought motor vehicle-based travel. Key instrumental and affective motor vehicle attributes were found to be central to the motor vehicle-based tourist experience including convenience, independence, freedom, reliability and road access while the nature environment and personal development were also important motivations. This study also found seven categories to represent the tourist experience in the Yukon including ‘unique opportunities and service expectations’, ‘travel adventure’, ‘pristine nature’, ‘engaging places’, ‘meaningful interrelationships and solitude’, ‘unexpected weather’, and ‘sharing ii stories’. Meaningful interrelationships and solitude were found to be the most consequential of the narratives because of the lasting impression they left in participants’ narratives. As visitor numbers increase in the Yukon by way of various marketing strategies, changes in tourist demand and the onset of climate change and warming temperature, this travel market will likely increase. Diversifying the drive tourism market in the Yukon by developing different products to match the different needs of various subgroups will be beneficial to tourism businesses as well as fostering sustainable practices. More studies like this one will be needed to track changes in tourists’ travel patterns and preferences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".