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Record W2942413995 · doi:10.1080/09669582.2019.1601732

Degrowing tourism: rethinking tourism

2019· article· en· W2942413995 on OpenAlexaff
Freya Higgins‐Desbiolles, Sandro Carnicelli, Chris Krolikowski, Gayathri Wijesinghe, Karla Boluk

Bibliographic record

VenueJournal of Sustainable Tourism · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDegrowthTourismThrivingGreen growthEquity (law)CapitalismSustainable growth rateEcotourismSustainable tourismAlternative tourismEconomic systemEconomicsPolitical scienceSustainable developmentSustainabilityPolitical economySociologyPoliticsSocial scienceLawEcology

Abstract

fetched live from OpenAlex

Concerns with growth have steadily advanced since the Limits to Growth report due particularly to human impacts on the natural environment. Since that time, neoliberal capitalism has become increasingly reliant on growth exacerbating these problems. The destructive outcomes of these strategies has led to a growing interest in degrowth. Analysts are examining how we can create economies that eschew a growth imperative while still supporting human thriving. Tourism as a key facet of capitalism is implicated in these issues and recent concerns with “overtourism” are only one symptom of the problem. This article presents a conceptual consideration of issues of degrowth in tourism. It examines current tensions in international mobility and argues just and sustainable degrowth will require greater attention to equity. This analysis suggests that essential to such an agenda is redefining tourism to focus on the rights of local communities and a rebuilding of the social capacities of tourism. This article argues for the redefinition of tourism in order to place the rights of local communities above the rights of tourists for holidays and the rights of tourism corporates to make profits.

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)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.037
Scholarly communication0.0130.014
Open science0.0020.009
Research integrity0.0030.005
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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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 designTheoretical or conceptual
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

Citations509
Published2019
Admission routes1
Has abstractyes

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