Bibliographic record
Abstract
The odes (Fu, a literary style in Han dynasty) to the capitals of Han dynasty, represented by Ban Gu’s Ode to the Two Capitals and Zhang Heng’s Ode to Two Capitals, depicted the image of Changan as the luxurious and magnificent capital city of Han dynasty. In such depictions, which excluded tangential details of Changan of the Western Han dynasty as well as other elements not consistent with the broad theme of luxury (such as the efforts at reconstruction of Changan during the reforms of Emperor Yuan and Wang Mang), the dynamics of historical transformation were molded into a static sample of a capital city, labeled with desire. On an emotional level, the authors of these odes were deeply attracted to this capital city of luxury and magnificence; on the other hand, however, their ideal capital city was not the charming and tempting Changan. The more they devoted their heart and soul to depicting those grandiose architectural works and the heavenly imperial gardens, the more skeptical they became of the domineering powers’ attempt at cultivating infinite desires; the harder they tried to glorify the awe of hunting, the more obvious their disapproval of the emperors’ pursuit of pleasure exemplified by hunting. Yet the intricate interplay of emotional attraction and rational criticism made the image of Changan even more vivid and colorful.
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 | 0.000 | 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".