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Record W9945537 · doi:10.1103/physrevb.38.7909

Strategic Management of Ecotourism: An Australian Perspective

2008· article· en· W9945537 on OpenAlexaboutno aff
John Saee

Bibliographic record

VenueJournal of tourism · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsEcotourismTourismEnvironmental ethicsNatural (archaeology)GeographyNatural resourceEnvironmental resource managementEnvironmental planningPolitical scienceEconomics

Abstract

fetched live from OpenAlex

In 2002, the United Nations declared the International Year of Ecotourism, whose peak event was the World Ecotourism Summit, held in Quebec, Canada in May of that year. Ecotourism has since presented many formidable challenges including the following: many of the world’s natural areas remain under threat; there has been a further loss of biodiversity and resources for conservation remain inadequate; world tourism arrivals have grown by 23% and are forecast to double by 2020; climate change has increasingly become a major threat affecting the very resources on which ecotourism depends – natural areas and local and Indigenous communities around the world; this has helped raise awareness of the contribution of current and future tourism operations and services to global climate change; the role of tourism in supporting sustainable development and the achievement of the Millennium; development Goals, notably the alleviation of poverty, has become recognized as a critical industry responsibility; ecotourism has articulated the core principles of sustainability in the travel and tourism industry and therefore plays a leading role within the industry as a whole (The International Ecotourism Society, 2007). With this mind, this research article deals with problems and emerging Trends associated with the Australian ecotourism marketing and how they are impacting Australia’s tourism patterns and also future trends for Ecotourism.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.094
GPT teacher head0.382
Teacher spread0.288 · 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.

Study designQualitative
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
Published2008
Admission routes1
Has abstractyes

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