Post-Pandemia at the Poissonerie Shanahan: An Account of Sick Cities and their Remedies
Bibliographic record
Abstract
An epidemic is not simply biological, but rather a spatial phenomenon that mutates sociopolitical constructions. The mysteries and fears associated with the lurid metaphors of disease have landscaped the city - the typical setting for those thought most susceptible to illness - as though we are looking at "the section of a fibrous tumour." This thesis speculates on the transformations of space and human relation through epidemic scales. Set in the fictional Poissonerie Shanahan in Montreal's Jean Talon Market as envisioned by the Quebecois novel, Nikolski, this research draws parallels between the tools of past urban epidemics and current morphologies as a result of COVID-19. By using fiction as a template to understand the intersections of architecture, urbanism and public health, the thesis chronicles an epidemic representation in order to exercise our empathetic intelligence in the face of a global crisis that has rapidly spatialized blame.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".