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Record W2077014331 · doi:10.1016/j.tourman.2014.08.009

Denying bogus skepticism in climate change and tourism research

2014· article· en· W2077014331 on OpenAlexaff
C. Michael Hall, Bas Amelung, Scott Cohen, E Eijgelaar, Stefan Gößling, James Higham, Rik Leemans, Paul Peeters, Yael Ram, Daniel Scott, Carlo Aall, Bruno Abegg, Jorge E. Araña, Stewart Barr, Susanne Becken, Ralf Buckley, Peter Burns, Tim Coles, Jackie Dawson, Rouven Doran, Ghislain Dubois, David Duval, David A. Fennell, Alison Gill, Martin Gren, Werner Gronau, Jo Guiver, Debbie Hopkins, Edward H. Huijbens, Ko Koens, Machiel Lamers, Christopher J. Lemieux, Alan A. Lew, Patrick Long, Frans Melissen, Jeroen Nawijn, Sarah Nicholls, Jan‐Henrik Nilsson, Robin Nunkoo, Alan Pomering, Arianne Reis, Dirk Reiser, Robert B. Richardson, Christian M. Rogerson, Jarkko Saarinen, Anna Dóra Sæþórsdóttir, Robert Steiger, Paul Upham, Sander van der Linden, Gustav Visser, Geoffrey Wall, David Weaver

Bibliographic record

VenueTourism Management · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsSimon Fraser UniversityBrock UniversityUniversity of WinnipegWilfrid Laurier UniversityUniversity of OttawaUniversity of Waterloo
Fundersnot available
KeywordsClimate changeMisinformationSkepticismTourismDenialConfusionPolitical scienceEnvironmental ethicsPsychologyEpistemologyLaw

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.151
metaresearch head score (Gemma)0.306
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.981
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1510.306
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0120.051
Scholarly communication0.0160.016
Open science0.0030.013
Research integrity0.0190.039
Insufficient payload (model declined to judge)0.0060.002

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 head0.557
GPT teacher head0.488
Teacher spread0.070 · 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.

Study designTheoretical or conceptual
DomainEvaluation
GenreCommentary

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

Citations35
Published2014
Admission routes1
Has abstractno

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