Bibliographic record
Abstract
이 논문에서는 한국계 미국인 아이들과 그들의 교육을 (재)정의하는 담론들의 역사적 불연속성, 균열과 예상치 못했던 연속성을 찾고 이는 이러한 담론에 의해 생산된 지식이 투명하고 진실되다는 것에 대한 믿음에 도전하려는 목적을 지닌다. 후기구조주의 접근을 통하여 한국계 미국인 아이들을 성취도가 높은 모범적 소수로 정의하는 지배적인 이미지와 내러티브의 정당성을 조사하고 도전하고자 한다. 또한 이 논문에서는 한국계 미국인 아동들에 영향을 미치는 황화론, 모범적 소수, 한국적임과 세계 시민성과 같은 서로 교차하는 담론들을 밝힌다. 이로 인하여 이 논문은 한국계 미국인 아동기를 이해하는 지배적인 방식에 대한 우리의 앎을 지우고자 한다.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".