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Record W2158724897 · doi:10.1016/j.tourman.2014.08.008

No time for smokescreen skepticism: A rejoinder to Shani and Arad

2014· article· en· W2158724897 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, Geoffrey Wall, David Weaver

Bibliographic record

VenueTourism Management · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of OttawaUniversity of Waterloo
Fundersnot available
KeywordsClimate changeTourismSkepticismDenialGreenhouse gasNatural resource economicsPolitical economy of climate changePessimismPolitical scienceEconomicsPsychologyLawEcology

Abstract

fetched live from OpenAlex

claimed that tourism scholars tend to endorse the most pessimistic assessments regarding climate change, and that anthropogenic climate change was a fashionable and highly controversial scientific topic . This brief rejoinder provides the balance that is missing from such climate change denial and skepticism studies on climate change and tourism. Recent research provides substantial evidence that reports on anthropogenic climate change are accurate, and that humaninduced greenhouse gas emissions, including from the tourism industry, play a significant role in climate change. Some positive net effects may be experienced by some destinations in the short-term, but in the long-term all elements of the tourism system will be impacted. The expansion of tourism emissions at a rate greater than efficiency gains means that it is increasingly urgent that the tourism sector acknowledge, accept and respond to climate change. Debate on tourism-related adaptation and mitigation measures is to be encouraged and welcomed. Climate change denial is not. .

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.521
Threshold uncertainty score0.900

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)0.0010.001

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 head0.199
GPT teacher head0.394
Teacher spread0.195 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations24
Published2014
Admission routes1
Has abstractno

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