Tourism, Place and Identity: Rural Tourism in IcelandandPrince Edward Island-Results of a Dialogue
Bibliographic record
Abstract
In recent years, there has been a dramatic increase in the number of tourists visiting Iceland, so much so that tourism density there now rivals that of Prince Edward Island. The nature of the tourism industry is broadly similar in both islands – generally seasonal, with a heavy stress on cultural and environmental resources. Both islands have made a concerted effort to utilize tourism as a community-development tool for the more rural areas, which has met with mixed success – and has raised a whole new set of issues, including oversaturation. In May 2017, a public symposium, hosted by the Institute of Island Studies at University of Prince Edward Island, in concert with the Rural Policy Learning Commons and Meetings and Conventions PEI, brought together experts on rural tourism from both islands to share stories and best practices and policies in dealing with tourism that in some ways – at least anecdotally – has reached and even surpassed its saturation point. Based on a dialogue generated by the Symposium, this paper provides a synthesis of the creative ways rural communities and policy makers on Iceland and Prince Edward Island have met the challenge on these two islands. It then offers an analysis (grounded in McElroy and Albuquerque’s 1998 article, “Tourism Penetration Index in Small Caribbean Islands,” Annals of Tourism Research 25(1), pp. 125-68) of how well those strategies may be working.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".