What does community do? Reconsidering community action on the Toronto Islands using assemblage theory
Bibliographic record
Abstract
Abstract This paper uses assemblage theory to consider the work that community does in a residential neighborhood in Toronto, Canada. It utilizes assemblage theory and connections between assemblage, affect, and emotion to advance an understanding of how community shapes capacity and action. The analysis shows how community has been enacted on the Islands, what actions and tendencies this assemblage makes possible or likely, and what it constrains. It also contributes to understanding what assemblage analysis can do. The mechanisms by which desire is channeled toward certain kinds of actions in the assemblage include the performance of community for self-preservation, the use of history and memory in the making of the community assemblage, and the role of territoriality, identity, and belonging in community-preserving actions. The analysis also reveals processes of stasis through reification of the assemblage and its interdependence with other processes like racial capitalism. Finally, I propose possibilities for shifting the assemblage, including telling different histories, and greeting emotional intensity experimentally. Seeing community through the lens of assemblage enables us to ask different questions, which may help us build the communities we need for a more just future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".