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Record W4246787621 · doi:10.1201/9781315209586-14

The Use and Social Enjoyment of Murals

2017· book-chapter· en· W4246787621 on OpenAlexaboutno aff
Susan Carden

Bibliographic record

VenueApple Academic Press eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsVisual artsArtPsychologyAesthetics

Abstract

fetched live from OpenAlex

The audience for murals includes the cultural tourists so desired by urban and regional policy-makers around the world. The association of murals with &s;the people,&s; the collaborative methods sometimes used to create them, their locations and content all imply a communal audience, a &s;public&s; rather than an individual viewer. Murals located inside public or municipal buildings as public art often address an imagined ideal citizenry, constructing particular visions of nationhood. The role of state patronage in the creation and management of murals is often controversial. Murals were undertaken as &s;small-town promotion projects&s; in the USA, Canada and New Zealand in the late twentieth century. While murals do not have the same relationship to the market as more easily tradable art forms, their site-specific nature means they can be commoditized as visitor attractions through the tourism industry. Revolutionary murals are commoditized and institutionalized. The enjoyment of murals by tourists does not neutralize their political complexity for local residents and authorities.

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)0.001
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.165
GPT teacher head0.352
Teacher spread0.187 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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