Bibliographic record
Abstract
Toronto’s Chinatown was born out of a form of resistance which paired infidelity to official definitions of Canadian citizenship (who was allowed to belong) with fidelity to its community members (who belonged). Historical representations have often been unfaithful to the Chinatown community, and architectural imagery has often tended to erase it from view entirely. In this essay, the authors explore Linda Zhang’s appropriation of architectural technologies (such as photogrammetry and pointcloud scanning) as a form of antidisplacement resistance to the ongoing and centuries-old erasure(s) of Toronto’s Chinatown. Her project, Chinatown 2050, uses speculative futurist 3D reconstructions and community storytelling to reimagine what Toronto’s Chinatowns might be like in the year 2050. Unfaithful to the present and past “official” demarcations of the neighborhood, it is a form of social organizing and imagination towards a more generative future. In countering technological acts of erasure, Zhang’s work illuminates the broader sociopolitical implications of technological choices and critiques the ways in which history often silences marginalized communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".