Review: <i>Performing Chinatown: Hollywood, Tourism, and the Making of a Chinese American Community</i>, by William Gow
Bibliographic record
Abstract
theme, or navigate the entries via an interactive map.Well-selected links take visitors to additional resources both within National Park Service webpages and beyond.Citations reference both primary sources and important secondary sources on each topic.While I might quibble with how some of the individual stories were organized by theme, each example is well constructed and together they argue compellingly that women built meaning in their ancestral or adopted homelands.Home and Homelands introduces unfamiliar stories.We learn about a Latina dying of childbirth on an overland trail moving north rather than west, a Tsimshian woman homesteader on the U.S.-Canadian borderlands, an African-American homesteader near Los Angeles, and a female missionary who feels more at home in a Hawaiian royal daughter's traditional hale than in her American husband's western-style adobe house.Objects related to white settler women tell stories of displacement and longing for other homes rather than the self-sacrificing homemakers celebrated in popular memory.Other objects tell stories not typically associated with western women.We learn about bohemian culture in Depression-Era San Francisco; the World War II Women's Overseas Shopping Service; Indigenous Chamorro women's wartime civilian incarceration in Saipan; and cultural persistence through kapa (bark cloth) making, language preservation, and oral tradition.Taken together, these object-based stories reveal the diversity of western women's experiences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".