Bibliographic record
Abstract
During the winter of 2020, Toronto resident Khaleel Seivwright, began to construct small mobile shelters to provide insulation and privacy to unhoused residents living outdoors. Conditions produced by the COVID-19 pandemic increased demand on the city’s already underfunded and strained shelter system, subsequently accelerating development of encampments in parks throughout the city. From the outset, these “tiny shelters” served as a flashpoint in public discourse on the question of the relative health, safety, and beauty of unhoused privacy. Drawing on media coverage of Seivwright’s case, we address the question of the private persona as it emerges in relation to the unhoused, and to the practices of violent expropriation which daily police their existence. By examining the news discourses produced about Sievwrights tiny shelters, we interrogate how Sievwright’s public persona came to represent encampment residents as well as himself subsequently emerging as a boundary subject mediating the contradictory relations immanent to domesticity: between public and private space and public and private identity. Our analysis asks how the limits of privacy are actively imposed and managed under capitalism: who is allowed to have domestic space, where is that domestic space allowed to exist, and crucially what public personas emerge in relation to practices departing from the normative bounds of capitalism’s public/private distinction? Using critical discourse analysis (CDA), we examine the ways in which public personas are mediated by individuals and media institutions at the same time as addressing how personas themselves intervene in this process. CDA directs us to ask how personas are constituted through language. This emphasis on persona as both outcome of relations of power and as mediator in its own right permits us to address figures who are conventionally denied personas while simultaneously complicating and challenging the meanings behind domesticity within capitalist cities.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.054 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".