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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 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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0100.019
Scholarly communication0.0230.019
Open science0.0010.004
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0340.009

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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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