MétaCan
Menu
Back to cohort
Record W4389954510 · doi:10.1177/10925872231217489

Effects of COVID-19 on Visitor Intentions to Attend Personal Interpretation Programs in the Provincial Parks of Alberta, Canada

2023· article· en· W4389954510 on OpenAlexafffundabout
Glen T. Hvenegaard, Elizabeth Halpenny, Clara-Jane Blye, Katherine Corrigan

Bibliographic record

VenueJournal of Interpretation Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVisitor patternPandemicInterpretation (philosophy)Coronavirus disease 2019 (COVID-19)PsychologySocioeconomicsMedical educationSociologyMedicine

Abstract

fetched live from OpenAlex

Research has examined COVID-19's impacts on parks, but little research has studied the pandemic's impact on in-person interpretation. Based on responses from 431 visitors to Alberta's provincial parks before and during the pandemic, this paper investigates how the pandemic affected visitor intentions to attend personal interpretation programs. Intentions to attend programs decreased after the pandemic started, but were greater for respondents who had attended programs the previous season. Key reasons for not attending programs were not to become infected and not to infect others. Intentions to attend programs were greater for males than females, and greater for respondents with an increased education and a larger household income. Despite pandemic concerns, 53% of respondents said that programs should be offered, with highest support for amphitheater shows, followed by guided hikes, point duties, and family events. Park managers should clearly communicate the benefits and safety measures employed for interpretation programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.358
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Interpretation ResearchSame topicUrban Green Space and HealthFrench-language works237,207