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Record W1661634093 · doi:10.1108/ijefm-12-2014-0027

Wild and banal: the value of the arts as commons

2015· article· en· W1661634093 on OpenAlexaboutno aff
Katya Johanson

Bibliographic record

VenueInternational Journal of Event and Festival Management · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsCommonsOriginalityThe artsValue (mathematics)SociologyPublic relationsFunction (biology)Public valueSocial mediaPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to identify the value of the arts play in public spaces in replicating a contemporary commons. Design/methodology/approach – The study is an exploratory investigation which uses a case study of cultural events in public parks – the Vancouver Parks Board’s fieldhouse residency program (2012-2015). The study uses content analysis of the social media sites created for these projects to identify how the sites and the cultural events were valued by stakeholders and participants. Findings – The paper finds that, in combination, the park events and the social media discussion of them function as a form of the commons, in which new urban communities are formed or defined around specific common social interests. Research limitations/implications – The paper finds that, in combination, the park events and the reflective engagement prompted by the social media discussion of them function as a form of the commons, in which new urban communities are formed or defined around specific common social interests. Practical implications – It is anticipated that cultural programs will increasingly interact with common public places. Social implications – The study supports the increased use of and recognition of public places as culturally significant. Originality/value – The study aims to encourage the expansion of arts and cultural policy and programs to incorporate common public places.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.116

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)0.0000.000

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 head0.029
GPT teacher head0.346
Teacher spread0.317 · 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
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

Citations1
Published2015
Admission routes1
Has abstractyes

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