Bibliographic record
Abstract
In the context of the current toxic drug supply crisis that has claimed the lives of over 52,000 people in Canada since 2016, the inaccessibility of publicly available washrooms, and the closure of life-saving supervised consumption sites across Ontario, there is a rising need for libraries and other public infrastructures to support the most vulnerable people in our communities. Following this thread, this dissertation takes up Berlant’s conception of “the commons” through three pragmatic and urgent case studies. Focusing on accessible washrooms and bookstores in Toronto’s gay village, Ontario’s supervised consumption sites, and a packed library school classroom, each of these three case studies locates the commons in a different style of constructed social space. On the surface, these three spaces are designed to support a positivist goodness and the infrastructures of social sanitization. However, as I unravel the implications of political rhetoric, geospatial coordination, public health policy, and moral sanitation attached to these zones of correctness, I complicate the “it gets better” narrative of forward social progress undergirding each of these case studies.Throughout this project, I argue for consistent and pragmatic action as a solution to the abject despondence produced by a crumbling infrastructure of care. Importantly, this is not a nullification of despair in the face of death and suffering, but an offering of action over hopelessness. Following the principles of harm reduction, I contend that harm is inevitable in any assemblage. Further, while harms cannot be eliminated, the magnitude of their impact can be reduced. And sometimes, that’s all we can do. Fundamentally, this project is about doing something, anything, to stem the flow of political and personal inaction in the face of seemingly impassable infrastructures. Fueled by an unrelenting dissatisfaction with how things are and an absolutely inarticulate vision of how things could be, this dissertation purposefully embraces the generative power of a push for something just a little bit better. Taking Berlant’s assertion of the commons as “an action concept” to heart, I detail the practical, real-world intervention I have applied to each case study and analyze the outcomes and implications.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".