Understanding the United States overnight pleasure travellers to Canada : a longitudinal approach
Bibliographic record
Abstract
The data for this study derived from the International Travel Survey (ITS) by Statistics Canada was used to explore and assess the US overnight pleasure travel market, better understand the market, and provide implications for tourism marketing practice. The data sets cover during the period from January 1990 to September 2005. The study objectives were: (1) to explore and monitor tourism trends and performance by the US overnight pleasure travel market utilizing tourism indices and to forecast future trends and expectations of the indices and (2) to identify the relationship between travel activities and other trip characteristic variables for positioning of the market. To accomplish the purposes of the study, trend analysis and forecasting using the tourism indices were performed longitudinally and correspondence analyses were conducted for the travel activity-based market positioning. The study identified usability and applicability of the ITS data for tourism marketing purposes. This study makes several major contributions to the travel market research literature, even though much further research should be undertaken to confirm and strengthen the theories and make use of the result in practice. First, it explored tourism trends and performance. Tourism indices utilized in this study, on the whole, were identified as useful tools to examine and monitor market changes and better understand the dynamics of international tourism in Canada by the US overnight pleasure travellers. Second, it identified the relationship between travel activities and other trip characteristics for positioning of the market and provided insightful implications for marketing practice. Finally, it identified usability and applicability of the ITS data for tourism marketing purposes by approaching longitudinal empirical research from analyses of statistical data. The ITS data could be successfully used to provide fundamental information for tourism marketing strategies because it is a large scale, ongoing survey that is able to monitor markets over the years. In conclusion, it is expected that this study will contribute to stimulating interest in this subject among academics and practitioners. Tourism researchers and professionals should analyze the data and supply knowledge to their pertinent end users for making decisions and implementing marketing strategies effectively. Tourism marketers or managers should be supported by managing knowledge on the basis of continuous feedback about tourist preferences, perception, attitudes and market trends to the destination. The destination can use the knowledge to determine its most effective marketing strategy and tactics in real time, as changes develop in the marketplace and new tourist demands arise.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".