An analysis on homecoming support project by employment expiration of foreign workers
Bibliographic record
Abstract
To solve the serious problem of insufficient manpower in medium and small scaled companies, our country allows introduction of foreign manpower for 5 business types such as manufacture industry, service industry, etc. and there are 226,825 people who entered by employment license system(E-9) in Sep. 2013. However, according to the results of investigation "actual status and difficulties of foreign manpower's employment of medium and small scaled manufacturing companies" of KFTA in 2013, 36.4% of respondents responded that current scale of introducing foreign manpower is insufficient to solve difficulties of insufficient manpower in industrial world and 37.7% of them responded that the quarter of new employment is insufficient. So, they hope that current quarter system of foreign manpower can be abolished. Meanwhile, introduction scale of foreign manpower was decided by 53,000 people so as to solve enterprise difficulties in manpower and vitalize economics this year, but illegal aliens are 39,623, more than 17.5%, in Sep. 2013 after employment expiration. Therefore, it becomes serious social problem. Government displays various support businesses so that foreign workers after employment expiration can return to their homeland legally. This thesis intends to find measures to maintain royalty for our country and support their smooth resettlement through the analysis on homecoming support business of foreign workers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".