How to Save Chinatown: Preserving affordability and community service through ethnic retail
Bibliographic record
Abstract
Chinatowns in North America have been especially hit hard by COVID-19, a reality of anti-Asian racist and xenophobic sentiment exacerbated by the global pandemic. The factors contributing to increased business closures, commercial vacancy, and gentrification in Chinatowns have existed before the pandemic and have only been exacerbated. In order to preserve Chinatowns, municipalities have enacted historic preservation and small business support measures, such as historic designations, technical assistance for businesses, increased permit scrutiny, and legacy business programs. This study investigates the difference in retail changes across three Chinatowns in Vancouver, San Francisco and Los Angeles both prior and during the COVID-19 pandemic. Concurrently, this study also examines the impact of retaining a legacy business program and other preservation measures on the retail landscape. Interviews with city officials, organizers, community institutions, and members of the business community were conducted along with an analysis of existing local programs, policies and reports. This study finds that measures taken through historic preservation, small business support, and pandemic relief have not significantly addressed core needs within Chinatown communities. The most effective forms of relief and preservation was affordable housing, community-ownership of commercial businesses, and direct assistance for commercial rent. This study also acknowledges that some Chinatowns are faring better than others due to the ability of the Chinese community to fight against to historic discriminatory planning practices such as urban renewal, slum clearance, and highway building. The impact of these histories is deeply intertwined with the survivability of ethnic retail within each distinct Chinatown, and depending on the strength of existing community ties that remain will inform how preservation policies should be enacted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".