Bibliographic record
Abstract
In my view, there is no comparative studies of Lee Sang, a Korean novelist and Saul Bellow, an American novelist in Korea. That`s because it`s not easy to find something in common between two writers. Lee Sang was born in 1910 and Saul Bellow was born in 1915. They lived their own lives and they didn`t know each other. But my special interest is Saul Bellow and Lee Sang has many common characteristics. The aim of this paper is to provide an overview of the same characteristics between Lee Sang and Saul Bellow. In exploring the question of same characters, this paper limited to consideration of Nalgae and Dangling Man. Lee Sang bears a curious likeness to Saul Bellow. Bellow and Lee Sang are externally and internally analogous to each other. Their similarities are divided into biological similarities and the similarities in plot. In biological aspect, they altogether lived very hard times in early lives and were financially embarrassed afterwards. They both were influenced by Fyodor M. Dostoevsky. It was the one main cause of their similarities. Their marriage lives were not happy and they had no good relationship with their wives. This was the result of the fact that they grew up without `desirable father image.` In Dangling Man and Nalgae, these stories are told by the first person narrator `Joseph` and `Na(=I).` There are no specific plots or accidents in these two stories. The main characters are doing nothing and just killing times. They couldn`t put away their childish acts like playing with matches and shoe polishing. Joseph and Na cannot make any change in their lives. These facts are the result of their isolation from the family and society. They both have identity problems, too. Joseph was a Canadian who wants to join the American Army. Lee Sang was a Korean who lived under the Japanese imperialism. They both are intellectuals and very speculative persons. They all suffer from environments. They also are men of no abilities in the matter of money. Above all their endings are very similar. The main characters admit their own lots and satisfied with their present situations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".