Making Race, Making Place: Racialization of Space and People in San Francisco’s Chinatown, 1860-1906
Bibliographic record
Abstract
Since the 1990s, geographers have been increasingly interested in the intersections between the construction of space and the construction of racial categories, or the racialization of space. In particular, geographers have examined how the racialization of space and the racialization of groups are co-constructed. Chinatowns throughout the globe have been a focus of geographers who are interested in looking at these intersecting processes. Previous studies have examined the racialization of Chinatowns both across space and across time, ranging from 19th century Vancouver to contemporary Singapore. In this regard, the racialization of San Francisco’s Chinatown from 1860-1906 has been largely unexamined. Using archival methods and discourse analysis, this thesis examines the co-construction of Chinese as a racial category and Chinatown as a place in San Francisco from 1860-1906. From these analyses, the themes of disease and vice emerged as the most salient, after which an iterative cycle of racialization-spatialization was ultimately identified. This supports the assertion by previous scholars of the critical role of space and place in the construction of racial hierarchies and racial identities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".