Bibliographic record
Abstract
(Purpose) The purpose of this study is to compare and analyze immigration policies in the United States, Canada, and Australia and to suggest implications for Korean immigration policies. (Design/methodology/approach) To compare immigration policies by country, this study divided the areas of comparison into (1) population change and immigration population, (2) types and characteristics of immigration policy in each country, and (3) the latest trends in immigration policy. (Findings) First, since the 1970s, the US, Canada, Australia and South Korea''s population birth rate has been declining continuously, while it is expected. the proportion of the elderly population is 23.0% in the US, Canada, 22.0% in Australia and 40.0% in Korea in 2060. Second, the United States, Canada, and Australia are expected to see population growth due to continuous influx of immigrants despite low birth rate and high age population, while Korea, which has limited immigrant inflows, is expected to decrease along with Germany and Japan. Third, the Australian and Canadian immigration policy focuses on technical migration (67.5%) or economic migration (55.6%), while the United States focuses on family migration (66.4%). In addition, it was found that all three countries set an upper limit on immigrants by type of immigration and manage the influx of immigrants selectively. Finally, the United States, Canada and Australia have established and used immigrant databases to understand the degree of economic and social impacts of immigration. (Research implications or Originality) Recent changes in immigration policy in the United States, Canada, and Australia include: (1) selecting customized migrants based on labor market demand, (2) expanding the employment of short-term migrant workers to meet immediate labor market demands, (3) actively attracting international students who can become technical migrants, (4) reform of refugee acceptance system, and (5) migrants'' local inflow policy.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".