Binational tourism in Niagara: insights, challenges and the future
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide insights to relevant theories, challenges and the future opportunities for binational tourism in the Niagara region. Design/methodology/approach A selected cross‐section of relevant publications including news media items are reviewed. Data gathered by a group of graduate students from Niagara College during their externships with the Binational Tourism Alliance, are utilized. Binational tourism initiatives from different parts of the world are analyzed. Findings This paper implies that in order to strategize binational tourism in Niagara, relevant theories, international best practices, historic developments of binational initiatives in Niagara as well as news media reports should be analysed. It concludes that Niagara's tourism industry, on both sides of the border, must continue to work in close partnership on their binational tourism strategy and binational positioning in order to remain competitive as a major tourist destination in the world. Originality/value Inputs from the Executive Director of the Binational Tourism Alliance of the Niagara region as a co‐author, enhances the practical value of this paper. Not much research has been undertaken on the topic of binational tourism in Niagara. Therefore, particularly, the tourism policy makers as well as other stakeholders of tourism in the Niagara region should benefit from this paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".