Bibliographic record
Abstract
The Graffiti Transformation Project was a City of Toronto, Canada sponsored programme funding ‘marginalised youth’ to paint over graffitied walls with public murals. I argue the imperatives driving the project extended beyond the reaches of policy concentrated on youth remediation, to include concerns of urban governance as a spatial and aesthetic problematic. I explore the manner in which practices of graffiti eradication and community mural making generated a set of calculations that were informed by globally mobile aesthetic norms and were, in turn, aesthetically informing. These calculations were used as an epistemological baseline for assessing, at least at the level of appearance, a host of urban problematics including Toronto’s desire to position itself globally as a functioning multicultural city. Turning to Jacques Ranciere’s thoughts about the space of political aesthetics, I draw on an ethnographic example to tease out a moment of aesthetic engagement in which youth artists interrupted the codes and practices associated with creative city entrepreneurialism to render another configuration of politics, another way of being social. Implications for broadening the scope of urban youth policy scholarship to include analysis of an aesthetic turn are considered.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.084 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".