Development of Activities of Chinese Organizations in Vancouver (1896-1923): Case Study of Chinese Benevolent Association
Bibliographic record
Abstract
The Chinese are currently the largest Asian national minority in Canada. Tthey make up about thirty percent of the total population of Vancouer. The Chinese have played an important role in the history of British Columbia. The first Chinese arrived on the shores of British Columbia in the first half of the 19th century. A significant increase of new Chinese immigrants comes with the construction of the Canadian Pacific Railway, which was built primarily thanks to hard work of Chinese workers. After completion of the railroad, a large proportion of immigrants settled in Vancouver, which in the early 20th century became the city with the largest Chinatown in Canada. However, the Chinese encountered resentment from Canadian society, which considered them to be representatives of the inferior race, and part of the white public even demanded their deportation and a total ban on the entry of new Chinese immigrants. However, the Chinese community came together and concentrated in Chinatowns. But, they constantly faced stereotypes from Canadian society. Various organizations and associations also began to emerge and they tried in any way to help the Chinese community. The Chinese Benevolent Association has become one of the most important organizations. This bachelor's thesis will therefore focus primarily...
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".