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Record W4415586398 · doi:10.21083/crrf.v29i1.7643

Tourism, Place and Identity: Rural Tourism in IcelandandPrince Edward Island-Results of a Dialogue

2025· article· W4415586398 on OpenAlexaff
Laurie Brinklow, Þóra Gunnarsdóttir

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsTourismAnnalsTourism geographyRural tourismPublic policyRural areaSustainable tourismRural history

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.262
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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