Public Pedagogy and the Archive: Information, Interruption, and Public Things amidst Canadian Housing Activism
Bibliographic record
Abstract
The Toronto Disaster Relief Committee (TDRC) was a coalition of housing activists active in the years between 1998 and 2012 (Monsebraaten, 2012; Toronto Disaster Relief Committee, 1998, 2012). Their efforts bore witness to the rise of dehousing (Hulchanski, 2000, 2010), and the associated trauma and deaths of those forced to live without housing. Upon their closure, they donated their collective files to the City of Toronto Archives. This paper articulates how the curation and public provision of their collected material operates as a significant form of public pedagogy. First, the TDRC files are what I term a “counter-archive within.” That is, nested within a conventional archive, often figured as a repository for colonial common sense, motivated by the preservation of state power (Stoler, 2002), lies a counternarrative that challenges the epistemic authority of neoliberal logic. Secondly, in tandem with monthly public events, such an archive resists the “re-scripting” (Edkins, 2003, p. xv) of trauma in commemorative practice; activists as archivists unveil the faults in state-managed temporal arrangements. And finally, the storing of such material in a public institution like the Toronto Archives ultimately foregrounds the archive as a public thing (Honig, 2017) that requires our contestation, care, and attention.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.060 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".