캐나다의 언어정책과 다문화주의-공용어와 계승어 교육을 중심으로-
Bibliographic record
Abstract
This article aims to describe Canada’s linguistic policy, its policy pertaining to multiculturalism and their application through the official language and heritage language education. What can Korea learn from this experience.In 1969, Canada adopted its official language act providing an official status to English and French language. This law gave to all citizens the right to receive services from the Federal Government in both languages. Regarding the official language education, we look at English as a second language for allophones and French as a second language for Anglophones in the province of Ontario. The English program is divided between ESL and ELD. The reason for this division is the influence that the first language literacy has on the second language.In 1971, the Trudeau government recognized multiculturalism as component of Canadian society ; and in 1988 the Multiculturalism Act was passed which gave allophones the right to preserve and develop their culture. Except for English, French and aboriginal languages, all languages spoken in Canada are considered as heritage language. Those languages are an important tool of communication within families. They are influenced by second languages (the official languages). Those heritage languages are recognized as important assets for the society. The school boards of the province of Ontario provide heritage language education once a week for two hours and half. Teaching rooms, teachers’ salaries and professional development resources are paid by the government.Finally, Korea can learn three main lessons from the Canadian experience. First, to prepare for a multicultural society it is necessary to develop a linguistic policy. Also, Korean as a second language for immigrant children should be managed from the viewpoint of bilingualism. Third, children’s mother tongue should be taken into account by the education system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".