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Record W2123422103 · doi:10.5539/jms.v4n3p65

Preserving Wilderness at an Emerging Tourist Destination

2014· article· en· W2123422103 on OpenAlexvenueno aff
Anna Dóra Sæþórsdóttir

Bibliographic record

VenueJournal of Management and Sustainability · 2014
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
FundersFerðamálastofa
KeywordsWildernessTourismWilderness areaGeographyDestinationsSolitudeAdvertisingRecreationMarketingBusinessPolitical sciencePsychologyEcologyArchaeology

Abstract

fetched live from OpenAlex

Iceland is an emerging tourist destination with a huge growth in tourist arrivals where wilderness as an importantpart of the attraction.But visitors travel into wilderness to experience naturalness and solitude so whenwilderness becomes a popular tourist destination these qualities are difficult to preserve. This research builds onquestionnaire surveys gathered among 3941 travelers at nine areas in the Highlands of Iceland where the aimwas to explore to what extent travelers experience wilderness in the Highlands of Iceland and whether theyexperience that the carrying capacity of the destinations in the Highlands has been reached. It furthermorediscusses the possible use limits of wilderness as an arena for tourism.The results show that despite substantialhuman influence travelers experience wilderness. Most travelers consider the number of tourists appropriate,although some warning signs are emerging as 40% of tourists consider that there are too many tourists in one ofthe areas. Visitors prefer simplicity and wish to keep the places as natural as possible, with one exception at themost visited destination. There the attitudes of visitors are more anthropocentric, favoring more humanizedlandscape and service. Using wilderness as a tourism product is a very challenging task in an emergingdestination where tourism growth is as fast as it is in Iceland.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.015
GPT teacher head0.316
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2014
Admission routes1
Has abstractyes

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