Bibliographic record
Abstract
Earn Hong(1881~1951) had spent the two third of his lifetime in the United States since he emigrated to Hawaii in 1904 until his death. He worked in the Korean National Association of North America, especially for the news organ, The New Korea. The newspaper contains hundreds of articles written under his pen names such as ‘Sailor of East Sea’, ‘Chusun’ and ‘Hae-ong’. These articles reveal his dedication to the cultural development of Korean-American society. In addition, he played an important role in raising funds for the independence movement with Chinese people in the South America as well as the North America including the Sates and Canada. As a devoted patriot, he kept a balance between tradition and innovation, making an effort to recover the independence of Korea. He generated literary works in diverse genre and fascinated readers with timely critiques and articles. Furthermore, his knowledge in Chinese classics and fluency in Chinese brought him literary reputation among Chinese people in States so that he was able to lead the Korean-Chinese Solidarity with Chinese people. His interest in literature and history led him to introduce the biographies of independence movement leaders, as well as the histories of the Korean National Association of North America and Korean-American immigrant society. Under the influence of Chang-ho Ahn, he actively participated in the Korean Young Academy from its establishment. He emphasized nationalism and opposed Socialism and Communism. Although he was not known as a supreme theorist, it can be said that he proposed reasonable and gradual opinions grounded on common sense.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.127 | 0.044 |
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".