Bibliographic record
Abstract
1. Introduction: Chinese Ethnic Business and Globalisation Erik Fong and Chiu Luk Part 1: Economic Globalization, and Community Development and Chinese Ethnic Businesses 2. The Chinese Language Media and the Ethnic Enclave Economy in the United States Min Zhou and Guoxuan Cai 3. Globally Connected and Locally Embedded Financial Institutions: Analyzing the ethnic Chinese banking sector Wei Li and Gary Dymski 4. The New Chinese Business Sector in Toronto: A Spatial and Structural Anatomy of Medium and Large-sized firms Lucia Lo and Shuguang Wang Part 2: Transnational Linkages and Chinese Ethnic Businesses 5. Globalization, Transnationalism, and Chinese Transnationalism Ivan Light 6. Business Social Networks and Immigrant Entrepreneurs from China Janet Salaff, Arent Greve and Siu-Lun Wong 7. From Batlers to Transnational Ethnic Entrepreneurs?: Immigrants from the People's Republic of China in Australia David Ip 8. The Cemetery of Huang Xuliang: Transnationalism and the Chinese Overseas in the Early Twentieth Century Michael Szyoni Part 3: Chinese Businesses, Local Urban Structures and Homogenization 9. Chinese Ethnic Economies With the City Context Eric Fong and Linda Lee 10. Business Owners and Workers: Class Locations of Chinese in Canada Peter S. Li Part 4: Homogenization: Place Attachment and Chinese Businesses 11. The Global-Local Nexus and Ethnic Business Location Chiu Luk 12. Going to Malls, Being Chinese?: Ethnic Identities among Chinese Youths in Toronto's Ethnic Economy Emi Ooka 13. Conclusion Chiu Luk and Eric Fong
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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 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".