‘Made up’ and evolving – or already set in stone? Producing geotourism in the Niagara Region
Bibliographic record
Abstract
What are the origins of niche tourism? The making of a form of niche tourism is explained by more than the economic forces of supply and demand and socio-demographic factors. An integrated web of classificatory practices, people, institutions, knowledge, experts, and place-based factors is involved. This web, derived from the ideas of philosopher Ian Hacking, constitutes a field of niche tourism production. Geotourism and geotourists appear as subjects of interest that become economically useful, thus inviting business-related interventions. Through the field of niche tourism production, geotourism in the Niagara Region becomes an analysable phenomenon; it is created and cultivated as opposed to being purely a consequence of economic forces and socio-demographic factors. The continued emergence of different forms of niche tourism warrants research that explores the origins of specific niches, including those currently taking shape. This paper proposes a conceptual framework that articulates previously unacknowledged dimensions of the creation of niche tourism. A range of practices and phenomena as well as human and institutional actors, as components of an interwoven field of production, play an underappreciated role constituting a form of niche tourism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".