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Record W2245796911

Local culture: a rural community’s most valuable capital asset?

2013· article· en· W2245796911 on OpenAlexfundno aff
E. Wanda George

Bibliographic record

VenueSaint Mary's University Institutional Repository (Saint Mary's University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
FundersMemorial University of Newfoundland
KeywordsBusinessAsset (computer security)FinanceComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

While smaller rural communities cannot compete in the same arena of larger, mega tourism destinations, they do have one competitive advantage – their specific and unique local cultures. This cultural element differentiates a community from all other communities. While some rural communities have turned local cultural festivals and traditional celebrations into tourism attractions that draw visitors to the community, the benefits of these attractions to the local rural community are generally limited. Others have taken more aggressive strategies to commodify specific aspects of living local culture, traditions, customs and heritage into products to be directly sold to and consumed by tourists, providing a steady flow of economic benefit to the community. In this sense, local culture is a capital asset. While not all rural communities have strong or substantial cultural dimension, there are many that do which have not yet realized its economic potential. Drawing from recent doctoral research and theory, this concept paper discusses the notion of local culture as cultural capital and community capital asset when considering tourism development.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2013
Admission routes1
Has abstractyes

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