Literary Canon Formation and Historiography: The ‘Rediscovery’ of Ming–Qing Women’s Poetry
Bibliographic record
Abstract
‘No nation has produced more anthologies or collections of women’s poetry than late imperial China’, according to Kang-i Sun Chang. Indeed, the open-access database of Ming–Qing Women’s Writings at McGill University Library includes 5239 women writers and 431 poetry collections. Yet virtually no trace of this phenomenon, or of these women writers, can be found in transcultural literary histories and anthologies of world literature published in the West in the twentieth century and beyond. How is this possible? The reason is not simply the lack of translations of many of the poems, but rather it has to do with the lack of canonization of these women poets in Chinese literary history until the late twentieth century, when they were ‘rediscovered’. This article investigates this neglect with the aim of showing that there were several different reasons for it, related to poetics, genre hierarchies, anthology editing practices, etc., in the imperial era, and to aspects of Chinese literary historiography in the twentieth century. Two women ci poets, Liu Shi and Qiu Jin, are briefly introduced to show that the reasons for their exclusion, as well as their later inclusion in the national literary canon, also need to be addressed on an individual level.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".