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Record W4229041710 · doi:10.33137/ijournal.v7i2.38611

What’s Politics Got To Do With It?

2022· article· en· W4229041710 on OpenAlexaffvenue
Usman Malik

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPublic artArt worldReputationInstallation artVisual artsAuthoritarianismPoliticsVisual arts educationContemporary artVisual literacyPublic spaceAestheticsArt methodologySociologySpace (punctuation)ArtPolitical scienceDemocracyThe artsLawComputer scienceEngineeringSocial sciencePerformance artArt historyArchitectural engineering

Abstract

fetched live from OpenAlex


 
 
 Street art is a type of visual art that is often created in urban environments, adorning the walls of buildings, streets, and other publicly viewed surfaces. Despite its unfair reputation as “vandalism,” street art is increasingly recognized as an expressive art form and a feature of urban visual culture. Challenging traditional conventions of art, street art is radical both in its production and display. In its production, street art is anti-authoritarian and reclaims public space; in display, street art engages with everyday onlookers dialectically. As street art has transformed the possibilities of artistic display and production, so too must libraries respond by providing the information literacy needed to study and understand street art within its sociopolitical contexts. Libraries can intervene by liaising between street artists and users to increase public awareness of street art and dismantle negative stereotypes, and by providing classroom instruction that connects information literacy with the sociopolitical aspects of street art. Finally, art libraries can communicate the risks in creating street art to protect potential practitioners.
 
 

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)0.004
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 score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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