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Enregistrement W2290247932

Cities, Tourism and Sustainability (presentation)

2016· article· en· W2290247932 sur OpenAlexaff
Geoffrey Wall

Notice bibliographique

RevueTourism, leisure and global change · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCruise Tourism Development and Management
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésTourismSustainabilityOperationalizationUrbanizationPopulationLegislationGlobalizationBusinessSustainable tourismPopulation growthGeographyEnvironmental planningEconomic growthPolitical scienceEconomicsSociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Although beaches and mountains may often come to mind when thinking of tourism, the world’s cities are among its most important tourism attractions. There are many reasons for this which will be outlined briefly in the presentation. Furthermore, places that are good to live in are also places that are good to visit for both residents and visitors are looking for similar things: a safe and healthy environment, efficient transportation, lively cultural expressions, good shopping and so on. Thus, at first sight there is compatibility between the interests of residents and tourists and they can reinforce each other. However, tourism can also be regarded as an agent of urbanization for tourists need a place to live, if only temporarily, and are users of scarce resources such as water and energy, and generate more waste per capita than residents. Thus, they place increased stress on infrastructure of all kinds. Tourism is a major form of global change and is also impacted by other forces of global change. Many of these forces are concentrated in cities where they are superimposed upon one another: population growth, migration, globalization, environmental change, including climate change etc., creating pressing multi-dimensional problems that are focused on cities. Sustainable development has been proposed, particularly since the publication of “Our Common Future” in 1987, as an enlightened approach to the future and it has been enshrined in much legislation at a wide variety of scales but it has proven to be a difficult concept to work with and operationalize. Single sector approaches, such as sustainable tourism, are focused too narrowly to guide the move towards sustainability adequately. The promotion of sustainable livelihoods is an important refinement but, to date, it has been applied mostly in small poor communities in the developing world and its wider applicability remains to be explored and justified. Fortunately, urban tourism has some attributes that make it potentially more sustainable than many other forms of tourism: it is less seasonal, many activities are undertaken indoors, it is supported by substantial business and VFR markets, and residents from the broader region and further afield are needed to support many of the high-order functions that are concentrated in cities. At the same time, many cities are located in coastal locations and are, therefore, likely to be exposed to a full range of problems associated with climate change, such as more and more extreme events (such as storms and heat waves), coastal erosion, floods and droughts, and so on. These will be all the more challenging in that the infrastructure available to deal with these situations is often antiquated and designed for an earlier age. Leaving aside questions of costs and benefits, technical and financial feasibility, political will and so on, at and at the risk of oversimplification, I suggest that questions of urban sustainability, of which tourism is a part, can be subsumed under two major headings: infrastructure and governance. Much infrastructure is out of sight (since it is often under the ground) and often out of mind until it fails, but questions of water and energy supply, waste disposal, drainage, as well as transportation, are fundamental to the operation of urban areas, including tourism. Innovations in governance will be need to deal with the complex multi-sectoral problems that will occur.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,537
Score d'incertitude au seuil0,866

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,305
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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