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Record W4210816706 · doi:10.1079/tourism.2022.0006

Private Land, Public Interest: Securing Community Access to Private Lands for Mountain Biking and Amenity Migration in Canada

2022· article· en· W4210816706 on OpenAlexaffabout
Jeff Wahl, Pete Parker

Bibliographic record

VenueTourism Cases · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsAmenityRecreationTourismEnvironmental planningGeographyBusinessGeneral partnershipLocal economic developmentLand usePromotion (chess)Environmental resource managementEnvironmental protectionEconomic growthPolitical scienceFinanceCivil engineeringEconomicsEngineering

Abstract

fetched live from OpenAlex

Abstract In order to support diversified forms of economic and community development, rural communities are increasingly investing in recreational opportunities on neighbouring lands, including agri-tourism and mountain resort development. However, in communities where significant proportions of peripheral land holdings are privately owned, opportunities for recreational land access and development are restricted or threatened. The Village of Cumberland, located on Vancouver Island, British Columbia, is home to a premier network of mountain biking trails developed by local users on a mix of both public recreational and privately owned industrial land. Without the existence of a formalized land use agreement between local public, private and not-for-profit land interests, the value of Cumberland’s mountain biking trail network cannot be wholly realized. This case study details the collaborative land use management partnership that has been developed in Cumberland in order to support economic and community development through amenity migration and the promotion of recreational mountain biking. Through sharing the costs and benefits of managing private land use, Cumberland’s multi-sector stakeholders have ensured that their individual strengths are utilized and unique interests are addressed. Information Vancouver Island University World Leisure Centre of Excellence Main image: Burns Lake bike trails By Britcruise - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=15729806 © Jeff Wahl and Pete Parker 2015

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.999

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 studies0.0020.000
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 head0.074
GPT teacher head0.319
Teacher spread0.244 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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