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Record W7096022165

BEST EN Think Tank VII Innovations for Sustainable Tourism The Community of Communicators and the Communication of Sustainable Development Management to Visitors of a National Park

2014· article· en· W7096022165 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentGovernment (linguistics)TourismSustainable tourismEco-efficiencyInstitutionOrder (exchange)Sustainable businessNational park
DOInot available

Abstract

fetched live from OpenAlex

In spite of the trend towards business as a key element in society and tourism, governments still play an important role in the sustainable development debate. Like any social institution, governments and related organizations do not always function effectively. When they do not, for some reason, we (academics, business people, government officials and the general public) often argue that the solution is to impose a “made in business ” solution. However, given the different goals of government and the business sector, that approach may not be appropriate or necessarily successful. This paper develops a “made in government ” solution to a public institution that was not functioning well. The solution had direct impact on the sustainable development of a major national park and international tourist destination – Banff National Park, Canada. It was a creative solution that also addressed a sustainable development issue; that of providing education to people in order to develop their knowledge of the sustainable development management of natural resources. Richard Sharpley (2000) identified one of the requirements for sustainable development to be “the adoption of a new social paradigm relevant to sustainable living ” (Sharpley, 2000, p. 13), which necessitates learning and hence communication.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2014
Admission routes1
Has abstractyes

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