What does community do? Reconsidering community action on the Toronto Islands using assemblage theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.057 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.020 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it