Marketing destination Niagara effectively through the tourism life cycle
Bibliographic record
Abstract
Purpose The purpose of this paper is to address reasons why destinations stagnate and lose visitor numbers and to offer a series of methods, which stakeholders can employ to assist with rejuvenation efforts. Design/methodology/approach The paper is based on a limited literature review of Butler's Tourism Area Life Cycle (1980). The academic theory is applied to the on‐going situation that is occurring in the Niagara region of Canada, although the insights are applicable to other tourism destinations that are facing stagnation and decline. Findings While Niagara tourism is currently experiencing a decline in visitor numbers brought about by a series of factors, the destination has the opportunity to rejuvenate its offering. Key components of the rejuvenation include collaboration, strategizing, developing a destination brand that resonates with existing and future visitors and incremental and revolutionary innovation. Once these key elements are in play, the destination should see visitor numbers rebound if not surpass previous high water marks. Originality/value This paper is of value to destination marketing officials and entrepreneurs who may believe visitation numbers are lower as a result of a variety of external factors including rising fuel prices, global warming, terrorism threats, changing passport regulations, SARS, hurricanes, tsunamis, and other concerns. By understanding the signals associated with stagnation, destination stakeholders will be in a position to take proactive actions designed to rejuvenate the destination.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".