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Record W2739719785 · doi:10.1080/14927713.2017.1353437

The effects of forest industry impacts upon tourist perceptions and overall satisfaction

2017· article· en· W2739719785 on OpenAlexaffvenueabout
Kyle W. Hilsendager, Howard W. Harshaw, Robert Kozak

Bibliographic record

VenueLeisure/Loisir · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsTourismCompromiseDestinationsPerceptionGeographyBusinessNatural (archaeology)Quality (philosophy)Tourist destinationsMarketingEnvironmental resource managementPsychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Due to the growth of nature-based tourism during in recent decades, the value of forested areas for the tourism industry has been increasing. This is largely due to the aesthetic values that are often associated with forests. However, forests are also highly valued for timber production, an activity that can seriously compromise the visual quality of forested landscapes. Therefore, this article examines the effect that forest industry impacts have upon tourist perceptions and overall satisfaction in destinations that promote natural landscapes to attract visitors. To help understand this issue, tourists were surveyed at natural attractions in Vancouver Island, Canada and Tasmania, Australia. Results suggest that certain forest industry impacts do have the potential to negatively impact upon tourist perceptions in the two destinations included for this analysis. However, there appear to be a number of additional elements that are also important for shaping the perceptions of tourists, as overall satisfaction ratings were shown to be quite high for both Vancouver Island and Tasmania.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.336
Teacher spread0.316 · 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

Citations1
Published2017
Admission routes3
Has abstractyes

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