Bibliographic record
Abstract
This article is dealing with the characteristics of Korean-American preschoolers who are aged four and five and quarter of the Korean language school population, and the roles of Korean language school. In order to understand them, we have interviewed the teachers and parents of the 4 year-old class of the school. Their abilities of using Korean are definitely related to their parents` abilities and their desire to educate Korean to them. Moreover, even though the students are only four year-olds, they are superior to use English to Korean. There are some roles of Korean language school. It should be a field to build their identities, to learn and practice Korean, and to experience Korean culture as a mother country. Lastly, it is important for the students and their parents to establish networks of Korean immigrant community in advance before the public education. Unfortunately, there has not been any Korean language education program for Korean-American children. Therefore, there should be efforts to establish appropriate education systems for the children and teachers alike. Especially, the curriculum of the programs for teachers should be focused on making children understand not only general ideas, but also the essence and quality of Korean language. And also, it should be able to show the prototype of Korean language education system for the children.(Kyung Hee University)
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".