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Record W4224293889 · doi:10.1080/08941920.2022.2065393

Inter-Group Perceptions of Key Performance Indicators for Monitoring and Evaluating Scenic Viewpoints

2022· article· en· W4224293889 on OpenAlexaff
Samantha Witkowski, Ryan Plummer, Gillian Dale

Bibliographic record

VenueSociety & Natural Resources · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsBrock University
Fundersnot available
KeywordsViewpointsVisitor patternPerceptionStakeholderPerformance indicatorEnvironmental resource managementBusinessKnowledge managementPsychologyComputer scienceMarketingPublic relationsPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

This study examines the perceptions of stakeholders in monitoring and evaluation (M&E) of scenic viewpoints. We statistically compare the perceptions of two different visitor groups (residents of the Niagara region and tourists) regarding key performance indicators (KPIs) for M&E at 12 different viewpoint sites in Niagara Parks. Visitor perceptions were also compared to environmental managers’ perceptions of the viewpoint sites. Results demonstrate that visitor groups do not differ in their overall perceptions of KPIs for viewpoints. Additionally, environmental managers and overall visitor groups significantly differ in their perceptions of KPIs for viewpoints. These results deepen the understanding of stakeholder perceptions of KPIs for environmental management of viewpoints, specifically the influential preferences and factors. They also highlight the importance to managers of considering diverse knowledge and values in decision-making, and ultimately enhancing efforts to manage scenic viewpoints.

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)0.000
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 score0.017
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.052
GPT teacher head0.257
Teacher spread0.205 · 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
Published2022
Admission routes1
Has abstractyes

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