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Record W2931522914 · doi:10.14430/arctic67944

“It’s All about the Scenery”: Tourists’ Perceptions of Cultural Ecosystem Services in the Lofoten Islands, Norway

2019· article· en· W2931522914 on OpenAlexvenueno aff
Bjørn P. Kaltenborn, Eivind F. Kaltenborn, John D. C. Linnell

Bibliographic record

VenueARCTIC · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersNorges Forskningsråd
KeywordsVisitor patternTourismRecreationEcosystem servicesCultural heritageDocumentationGeographyEnvironmental resource managementRevenueTourism geographySustainabilityCultural tourismEnvironmental planningMarketingBusinessPolitical scienceEcosystemEcology

Abstract

fetched live from OpenAlex

The Lofoten Islands in northern Norway face challenges from increasing visitor numbers, congestion, environmental impacts, and growing host-visitor tensions. Benefits include increased local employment and growing revenues. Future tourism policy requires better documentation of the non-economic benefits and values associated with tourism in Lofoten; this information is important to the development of policy and management processes. We conducted 45 in-depth interviews with domestic and international visitors, using the cultural ecosystem services (ES) framework to ascertain the core elements of the tourism experience, as well as views on management needs and development. We probed reflections on place, aesthetics, recreational opportunities, inspiration, social relations, cultural heritage, knowledge, spirituality, and identity by offering a combination of statements and questions. All these categories of cultural ES were important to most visitors. However, the importance of the landscape was paramount. Policy implications include the need to include landscape in ES assessments, to map places of especially high scenic value, and to use the ES framework more extensively to identify and compare non-economic and economic tourism values and benefits.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.224
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2019
Admission routes1
Has abstractyes

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