기획특집 : 다문화 사회의 문제와 대응논리 ; 다문화가족지원센터 종사자의 역량강화 방안
Bibliographic record
Abstract
This reports judged the workers in the Multicultural Family Support Center as specialists of multicultural Policy and tried to find way for their Strengthening capacity. They acted as intermediaries to help communicating well with each other between Korea sociey, the local people and settles. But State Administration of Korea actually did not control the workers in the field for carrying out the plans of the Multicultural Family Support. The governments of Canada and Japan are cultivating them for supporting in the Multicultural Family Support Center. Now we have to establish a effective network between the central, local government, colleges, research institutes and the Multicultural Family Support Center and also operate the training course for the workers in the light of the local properties. We should help them to take pride as specialists and to have professional expertise through the training course for Multicultural Family Support. If they could accumulate lots of experiences and know-how from this process, The Multicultural Policy of Korea gorvonment eventually would succeed. But We still have many problems to solve for buliding stable Multicultural Support such as quarantee of reasonable salary for the workers, reinforcement of personel according to the specialties, and the development of education system for the persisent Empowerment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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".