A Geographical Analysis of the Rates of Non-Travel Across the Regions of Canada
Bibliographic record
Abstract
The purpose of this study was to classify and segment non-travellers across the regions of Canada. This was achieved by empirically testing Haukeland's (1990) Haukeland, V. J. 1990. Non-travellers: The flip side of motivation. Annals of Tourism Research, 17: 172–184. [CSA][CROSSREF][Crossref], [Web of Science ®] , [Google Scholar] model of non-travel. The study revealed that while Quebec had the lowest incidence rate of non-travel, those who did not travel were socially constrained at a much higher rate than in the other regions of Canada. Those not born in Canada were most likely to be both financially and socially constrained and concentrated primarily in Ontario. Non-travellers in Western Canada were most likely to report being financially constrained. Overall, this study found that Haukeland's (1990) Haukeland, V. J. 1990. Non-travellers: The flip side of motivation. Annals of Tourism Research, 17: 172–184. [CSA][CROSSREF][Crossref], [Web of Science ®] , [Google Scholar] model assisted in the discovery of regional differences in non-travellers across Canada. If non-travellers are understood more clearly, some of their market potential could be realized by the tourism industry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".