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Record W3028859929 · doi:10.1177/0276237420951415

Aesthetics of Graffiti: Comparison to Text-Based and Pictorial Artforms

2020· article· en· W3028859929 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEmpirical Studies of the Arts · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsYork University
FundersFonds Wetenschappelijk OnderzoekKU LeuvenRadboud UniversiteitVlaamse regeringYork University
KeywordsGraffitiAttractivenessPaintingSalientArtValue (mathematics)Visual artsSpace (punctuation)AestheticsPsychologyArtificial intelligenceComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Graffiti art is a controversial art form, and as such there has been little empirical work assessing its aesthetic value. A recent study examined image statistical properties of text-based artwork and revealed that images of text contain less global structure relative to fine detail compared to artworks. However, previous research did not include graffiti tags or murals, which reside in the space between text and visual art. The current study investigated the image statistical properties and attractiveness of graffiti relative to other text-based and pictorial art forms, focusing additionally on the role of expertise. A series of images (N = 140; graffiti, text and paintings) were presented to a group of observers with varying degrees of art interest and expertise ( N = 169). Findings revealed that image statistics predicted attractiveness ratings to images, and that biases against graffiti art are less salient in an expert sample.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.189
GPT teacher head0.389
Teacher spread0.200 · 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