Topic Modeling the Hàn diăn Ancient Classics (汉典古籍)
Bibliographic record
Abstract
There is a small but growing literature on large-scale statistical modeling of Chinese language texts. Ouyang analyzed a corpus of over 40,000 ancient documents downloaded from multiple sources. This was used to plot the temporal distributions of word frequencies and geographic distributions of authors. Huang and Yu modeled the SongCi poetry corpus, first converting it to tonally marked pinyin to conserve poetically important pronunciation information. Nichols and colleagues reported initial modeling of the Chinese Text Project corpus1 in a conference paper. (Further below, we describe differences between this corpus and the Handian.) With additional collaborators, this group has now conducted two studies that are currently unpublished but under review. In the first, they apply topic models to address scholarly questions about the relationships among important texts of Ancient Chinese philosophy. In the second, they use topic modeling to investigate the concepts of mind and body in ancient Chinese philosophy. Although we share similar scholarly objectives with these researchers, our approach in this paper is unique in that for the first time anywhere we bring the benefits of computational modeling of ancient Chinese texts to a robust public platform that is mirrored on both sides of the Pacific. Besides being just a useful portal to the texts, our approach foregrounds the interpretive issues surrounding topic models, and makes more sophisticated exploration and analysis of interpretive questions possible for experts and novices alike.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".