Theoretical Approaches to the Collection and Appraisal of Graffiti Ephemera: A Toronto, Ontario, Case Study
Bibliographic record
Abstract
This article explores aspects of the archival value of graffiti and street art. It applies an intersectional feminist lens and draws upon Althusserian subject interpellation to elaborate some potential techniques for appraising graffiti. Understanding graffiti as ideological calls can help us understand how graffiti acts on us and functions for us: as a record of oppression, institutional and social relations, and individual negotiations with power. The article offers examples of graffiti acting as a form of speech for the unheard and marginalized and illustrates these examples with photographs of graffiti captured by the author and images of graffiti and street art located in various collections. It compares the appraisal and collection methods of the Urban Art Mapping Project, a North American participatory graffiti archive, with the Street- ARToronto (StART) street-art map maintained by the City of Toronto. Through these comparisons, the article argues for the evidential value of locally driven archives of the type of political graffiti excluded from the StART map.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.055 | 0.027 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".