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Record W7071852539

Visitors’ willingness to pay for interpretive services in Alberta parks

2021· article· en· W7071852539 on OpenAlexaboutno aff

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsWillingness to payInterpretation (philosophy)Natural heritageCultural heritageNational parkInvestment (military)Value (mathematics)Action (physics)
DOInot available

Abstract

fetched live from OpenAlex

Interpretive services provided at protected areas can add value to visitors’ experiences. They can also serve as conservation management tool in mitigating negative behaviours and inspiring pro-environmental action through educational and entertaining content and delivery. With shrinking investment in conservation from public coffers, protected area managers are increasingly forced to charge for specific services such as interpretation. While some research has examined visitor’s willingness to pay park fees, far fewer have examined WTP for interpretation. A sample of 730 visitors to four Alberta, Canada World Heritage sites was used to investigate visitors’ willingness to pay for park interpretation services. In-person delivery and specialized content garnered higher levels of WTP for park interpretation services. Wealthier visitors were more willing to pay fees, as were those who had already invested time (i.e., involvement) to learn about cultural and natural heritage (i.e., WH experts). Unexpectedly, education, travel with kids, and travel motives related to learning about cultural and natural heritage, were not significantly related to WTP for park interpretation services.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.720

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.199
Teacher spread0.174 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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