Lady Avengers in Jin He’s (1818–1885) Narrative Verse of Female Knight-Errantry
Bibliographic record
Abstract
Jin He 金和 (1818–1885), a pioneering poet of mid-nineteenth century China, wrote in a colloquial style strongly influenced by the ballad tradition. Jin’s style was prose-like and broke all the structural limitations of earlier poetry in order to create formal innovations, while at the same time experimenting with new subject matter. Liang Qichao 梁啟超 (1873–1929) and Hu Shi 胡適 (1891–1962) considered Jin He and Huang Zunxian 黃遵憲 (1848–1905) to be the major poets of the nineteenth century. Jin had a major impact both on other late nineteenth-century poets and on the “Poetic Revolution” that led to the rise of modern Chinese literature. However, his verse has been largely ignored ever since. Among the most striking contributions Jin made to the literary transition in the nineteenth century was his innovation in presenting the female knight-errant 女俠 (nuxia). This invented image of the female knight-errant reflected a new tradition of women’s voices in the literary works of his time, and had a great impact on the representation of swordswomen in modern literature. This paper examines how the image of nuxia in Jin’s writing is distinct from those found in past poetry, how the female knight-errant in Jin’s works inverts conventional gender norms, and how Jin’s female knight-errant image is both connected with and distinct from those in other literary forms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".