Bibliographic record
Abstract
Only a few decades ago, Western scholars of comparative literature tended to argue that any English-Chinese comparison was “futile or meaningless” (Yu, 162). As this discipline evolves, however, this previous notion is being replaced by the perspective that “a glimpse of the otherness of the other can produce new perspectives on our own faces in the great mirror of culture” (Hayot, 90). My thesis contributes to this stream of innovation by bringing into comparison the function of the moon in Su Shi’s “Water Melody” and in Samuel Coleridge’s “Dejection: An Ode”, finding that in both poems, the moon functions to foreground the poets’ psychological experiences and acts as an agent in the resolution of emotional conflict in the poems and lives of the poets. The purpose of this work is to broaden the field in which both English and Chinese poetry are understood to exist by examining each through the lens of the other. Both “Water Melody” and “Dejection” have been examined to the point of exhaustion in each of their relative traditions, but bringing them into new light may reveal previously unseen angles. For example, this research finds that Susan Stewart’s theory of eighteenth – century English nocturnes is highly compatible with twelfth-century Chinese nocturnes, and this foreign theory can breathe new life into an ossified conversation. In a dissonant example, the familiar Western associations of the moon as an evil omen, recalling vampires and werewolves, can feel bizarre when imagined from the perspective of Chinese associations of the moon with family reunion. This comparison, in addition to exploring these two poems and poets, ultimately creates a destabilizing effect by which a reader may be induced to move beyond the traditions, to a point where Weltliteratur is no longer the goal, as it was for Goethe, but instead a starting point.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".