Analysis on differences in Canada and China's official attitude and perception on their minority nationalities
Bibliographic record
Abstract
The government’s perceptions and attitudes of their ethnic minorities are in close relation with the ethnic minorities’ welfare policies, and also affect the public’s perception of ethnic minorities. Therefore a government’s definition and attitudes are crucial to maintaining national stability. For instance, Canada is a multi-nation state, comprising multiple ethnic groups in one country, with the two most influential as the French-Canadians and the English-Canadians. French and English Canadians are majority ethnic groups while there are many other minority ethnic groups such as the First Nations. The People’s Republic of China is also a multi-nation state, although the biggest ethnic group, the Hans, comprise 98% of the entire population.11 Although all nations have their own cultural cognition - common descent, history, culture, and language - both Canada and China have their own unique definition for their minority nations: Canada’s minority nations are the Aboriginal People of Canada 22, and China’s minority nations are the 55 officially recognized ethnic groups other than the Han people. This essay aims to compare the official perceptions and attitudes of ethnic minorities in China and Canada, hoping to clarify the relationship between ethnic minorities and mainstream ethnic groups, and help the general public to understand them, hence promoting harmonious societal development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".