Seasonal Price Adjustments in Winter Tourism: Impact on the Car Rental Market in Canada (2015-2022)
Bibliographic record
Abstract
This research explored seasonal price adjustments within the car rental market, offering a comprehensive analysis contextualized within the sphere of winter tourism in Canada from 2015 to 2022. Through an exploration of historical trends, consumer behavior, and market forecasts, the study uncovers the cyclical pattern of price fluctuations closely tied to the peaks and troughs of winter tourism demand. Noteworthy transformations within the winter tourism industry during this period, coupled with the increased reliance on car rentals for accessing remote winter destinations, underscore the pivotal role of seasonal price adjustments in shaping the accessibility and affordability of winter experiences. The implications extend across stakeholders, prompting car rental companies to balance profitability and competitiveness, while policymakers are urged to craft adaptable regulatory frameworks. Industry stakeholders are guided by insights to develop customer-centric strategies, embracing technology and sustainability for enhanced resilience. The study serves as a stepping stone for understanding the symbiotic relationship between winter tourism and the car rental market in Canada, providing valuable insights for industry adaptation and innovation.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".