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Record W2290247932

Cities, Tourism and Sustainability (presentation)

2016· article· en· W2290247932 on OpenAlexaff
Geoffrey Wall

Bibliographic record

VenueTourism, leisure and global change · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTourismSustainabilityOperationalizationUrbanizationPopulationLegislationGlobalizationBusinessSustainable tourismPopulation growthGeographyEnvironmental planningEconomic growthPolitical scienceEconomicsSociology
DOInot available

Abstract

fetched live from 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.866

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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