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Mountain Bike Tourism and Community Development In British Columbia: Critical Success Factors for the Future

2014· article· en· W1983526363 on OpenAlexfundaboutno aff
Ray Freeman, Eugene Thomlinson

Bibliographic record

VenueTourism Review International · 2014
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
FundersRoyal Roads University
KeywordsTourismStakeholderDelphi methodSustainable developmentSustainable tourismCritical success factorBusinessEnvironmental planningCommunity developmentEnvironmental resource managementSports tourismGeographyTourism geographyMarketingPolitical scienceEconomic growthPublic relationsComputer scienceEconomics

Abstract

fetched live from OpenAlex

Mountain bike tourism may provide significant benefit to communities, as shown by economic impacts of $38 (CDN) million realized in the Sea-to-Sky Corridor region of British Columbia, Canada in 2006. This achievement was realized due to regional stakeholder collaboration and community mountain bike trail planning dating back to the early 1990s. Despite the value and recent growth in mountain bike tourism, formal research into community mountain bike tourism development is still in its infancy. To explore the critical success factors necessary to build sustainable tourism capacity for the development of mountain bike tourism, a modified Delphi method was utilized to query industry experts in this empirical study. A literature review, followed by online surveys of selected experts, led to the creation of a framework for community-based mountain bike tourism development to support sustainable community mountain bike tourism strategies. This framework may be beneficial to clusters of tourism stakeholders for mapping-out long-term objectives and to achieve planning and operational efficacy. Utilization of the framework may also assist stakeholders to more effectively execute a successful community-based mountain bike tourism development strategy while assisting future researchers to delve further into an analysis of the role of critical success factors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
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 communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.331
Teacher spread0.306 · 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 designObservational
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

Citations33
Published2014
Admission routes2
Has abstractyes

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