Bibliographic record
Abstract
산업사회에서 후기 산업사회에로 전이되면서 선진 각국의 노동시장 수요도 단순 노동력이 아니라 숙련기술을 가진 고급 인력의 확보가 절실히 필요하게 되었다. 특히 캐나다처럼 광대한 국토에 비해 소수의 인구, 출산율 저하 그리고 경제성장에 따른 노동인력 수요가 급증하는 상황에서 이민유치는 국가적 현안이 될 수 밖에 없다. 1967년 캐나다는 과거의 폐쇄적, 인종차별적 이민정책을 획기적으로 개선한 이래 전 세계에서 가장 다양한 인종과 문화가 공존하는 사회로 변모되어왔다. 본고는 최근 이민에 대한 관심이 급격하게 높아진 가운데 지난 5년간 한국인들에게 가장 선호하는 이민대상국으로 떠오른 캐나다의 이민정책변화의 주요 동인들과 현황을 규명함으로써 캐나다 이민정책의 문제점들과 향후 전망을 분석해보고자 한다.\n\n\n\nContemporary societies are transforming from industrial to post-industrial societies, creating a demand for highly-skilled and specialized workers in the labor market to keep in step with the forces of change in our global community. Canada has the second largest land area in the world, but a relatively small population. With declining birth-rates, and increasing labor market demands as a consequence of economic development, attracting qualified and skilled immigrants has become an urgent and crucial issue for the Canadian national agenda. In 1967, Canada witnessed a significant reformed in her immigration policy, from one that promoted a closed-doors and racially discriminative policy, to one that advocated an open-doors and racially non-discriminative policy, which lead to the development of the most diversified society in the world, in terms of racial and cultural makeup. Recently, there has been a rapid increase of Koreans interested in emigrating, and it was found that Canada was the most popular destination for Koreans, for the last five years. This paper will analyze the primary motives for the changes in Canadian immigration policy, in light of current social trends and situations, and examine the implications and impact of the current Canadian immigration policy for the future, within the context of Koreans immigrating to Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.111 | 0.056 |
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".