Chinatowns around the world : gilded ghetto, ethnopolis, and cultural diaspora
Bibliographic record
Abstract
Introduction Chinatowns around the World Bernard Wong Chapter One Vancouver in Transition Peter S. Li and Xiaoling Li Chapter Two From Mott Street to East Broadway: Fuzhounese Immigrants and the Revitalization of York's Kenneth J. Guest Chapter Three The Trends in American The Case of the Chinese in Chicago Huping Ling Chapter Four Sydney: A Window on the Chinese Community Christine Inglis Chapter Five The in Peru and the Changing Peruvian Chinese Communities Isabelle Lausent-Herrera Chapter Six Havana: One Hundered and Sixty Years below the Surface Adrian H. Hearn Chapter Seven Problematizing Chinatowns: Conflicts and Narratives Surrounding Chinese Quarters in and around Paris Chuang Ya-Han and Anne-Christine Tremon Chapter Eight Chinatown-Lisbon? Portrait of a Globalizing Present over a National Background Paula Mota Santos Chapter Nine Ikebukuro in Tokyo: The First New Chinatown in Japan Yamashita Kiyomi Chapter Ten A Reflection Tan Chee-Beng
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".