Национальная идентичность в современной китайской прозе
Bibliographic record
Abstract
Посвящена вопросу выявления национальной идентичности в современной китайской литературе. Исследование проведено с учетом особенностей китайского национального самосознания и характера, историзма, культурного синкретизма конфуцианской, даосской и буддистской традиций, природы китайской системы письма и понятия «вэнь». Очерчены различия между этнической и национальной идентичностями, а также проблема самоидентификации автора на примере китайской литературы зарубежья. Особое внимание уделяется изучению прозы китайского зарубежья на примере творчества современной писательницы Чжан Лин (1957). Теоретической базой исследования являются работы Г. Д. Гачева, М. Н. Корнилова, В. М. Пивоева, Н. А. Спешнева и др. = The article deals with the searching for national identity of contemporary Chinese literature, based on the historical thinking, peculiarities of the Chinese national self-consciousness, cultural syncretism of Confucian, Taoist and Buddhist traditions, specific features of Chinese national character, nature of the Chinese writing system, wen concept. The differences between ethnic and national identity is also regarded in this article along with the problem of author’s self-identification on the example of Overseas Chinese literature. Special attention is paid to the learning of Overseas Chinese literature’s prose. Besides, it focuses on the works by Zhang Ling (1957), one of well-known Chinese fiction writer in Canada. We address theoretical studies by G. D. Gachev, M. N. Kornilov, V. M. Pivoev, N. A. Speshnev.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".