Bibliographic record
Abstract
At the same time as transnational migration has become common worldwide, the number of immigrants to the Republic of Korea has increased greatly. The number of immigrants was approximately 5,180,000 in the year 2005 alone (table 1). This represented a 77.2 per cent increase since 1995, or an average annual rate of increase of 5.7 per cent over the past 10 years. Within this general trend, it is important to note that the change in the number of male and female migrants differs greatly. While the number of males migrating to the Republic of Korea since 1995 increased by 44.3 per cent, there was an increase of 150.5 per cent in female migrants during the same period. Since the mid-1990s, females have accounted for a majority of the increase in the number of migrants entering the country. Within the trend of feminization of migration1, there is also a specific feature in the trend of female migration to the Republic of Korea. When viewing the type of visa issued according to sex, excepting the E-6 visa (arts and entertainment), employment-related visas2 are mostly issued to males and recently these numbers have been rapidly increasing, whereas the proportion of work-related visas issued to females is decreasing, showing a great gap in the type of visa issued between the genders (table 2). On the other hand, the E-6 visas (arts
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.191 | 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".