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Record W4296663356 · doi:10.1079/tourism.2022.0032

Social Sustainability and Community Development Practices: The Banff Centre for Arts and Creativity

2022· article· en· W4296663356 on OpenAlexaffabout
Christin S. Collishaw

Bibliographic record

VenueTourism Cases · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsSummitIndigenousCreativityThe artsExcellenceNational parkPopulationPolitical scienceEconomic growthPublic administrationSociologyGeographyLawArchaeologyEcology

Abstract

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Abstract The following is a case study of the Banff Centre for Arts and Creativity in Alberta, Canada. The Banff Centre for Arts and Creativity (Banff Centre) is located within the unique setting of the land known as Banff National Park, the first national park established in Canada (Parks Canada, 2018). Banff National Park lies within the traditional territory of the Treaty 7 First Nations, part of Canada’s larger Indigenous population who have been subjected to centuries of racism and exploitation by European settlers and their descendants. Truth and reconciliation policies and processes have received increasing attention in Canadian society, although actual practices often fall short of these principles.An aspiring leader in arts and creativity, the Banff Centre goes beyond its educational programming to engage in community development activities which support the United Nations’ Sustainable Development Goals (SDGs) and the principles of the United Nations Declaration on the Rights of Indigenous People (UNDRIP). The Banff Centre hosted a Truth and Reconciliation Summit in 2016, and followed up this summit with ongoing speaker series, strengthening relationships between locals and Treaty 7 First Nations who have lived in the area for thousands of years. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © C.S. Collishaw, 2021

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0420.000
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 head0.056
GPT teacher head0.370
Teacher spread0.314 · 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
Published2022
Admission routes2
Has abstractyes

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