Cascade Effect or Co-evolution within Tourism in the Niagara Region?
Bibliographic record
Abstract
Tourism is often galvanised around a central theme based on a region’s strengths in product supply and promotional opportunity, which usually results in an identifiable regional brand. However, this also hides the existing heterogeneous nature of tourism supply, especially in regions with an established brand. Securing long-term community economic development requires a broader focus since some unheralded tourism development paths may prove resilient over the long-term and ultimately contribute to community development. This paper investigates the less central stakeholders in the Niagara region of Canada and explores how future studies might integrate marginal tourism stakeholders in studies of the regional tourism economy. Through semi-structured interviews with regional tourism stakeholders, the analysis reveals a new perspective on tourism in the Niagara region by focussing on the place of marginal stakeholders in a region with a strong tourism brand. The region exhibits strong path dependence based on its industrial and agricultural legacy but long-term, organic, incremental processes of change within the region are creating new tourism development paths. These new paths co-evolve with the dominant tourism paths as well as other community development initiatives leading to positive change across the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".