Sympathy for the abject: (re)assessing assemblages of waste with an embedded artist-in-residence
Bibliographic record
Abstract
The assemblages of (post)industrial neoliberal society include the production of vast quantities of post-consumer materials categorized as waste, which for many appears to vanish from everyday spaces. But rather than make it disappear in any final sense, recycling and disposal processes simply move it in new forms into new places within the global flows of waste. In urban contexts, the abject category of waste must be expelled from the sanitary spaces and subjectivities of daily routines, yet its corresponding absence in everyday perception obviates the urgency of action. One approach to unbundling the abject residue of contemporary society without instigating castastrophic rupture of social orders is through the aestheticization of the expelled. Artists working in the realm of abjection can serve as agents disrupting and redefining boundaries and social imaginaries of the status quo. In this paper we examine an artist-in-residence at the Edmonton (Canada) Waste Management Centre, arguing that the Deleuzian assemblages erasing the material consequences of garbage can be short-circuited in a municipal setting by redistributions of aesthetic experience. In this case, the artist residency embedded in human and mechanical assemblages of waste disposal allowed the artist to transform materials into an aesthetic spectacle recategorizing the abject as sympathetic and a vital component of social and spatial discourse.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".