Drawing Out the Essentials: Historiographic Annotation as a Textual Network
Bibliographic record
Abstract
Abstract During the Liu-Song 劉宋 dynasty (420–79), Pei Songzhi 裴松之 (372–451) compiled an elaborate set of annotations to the history of the Three Kingdoms era, Sanguozhi 三國志. Several decades later, during the Liang 梁 dynasty (502–57), Liu Xiaobiao 劉孝標 (462–521) compiled similar annotations for Shishuo xinyu 世說新語, a collection of pithy anecdotes concerning prominent figures from the Han through Jin dynasties. These annotations were products of a new era of textual production, in which fervent interest in historiography and book collecting reached new heights. Though building on earlier traditions of commentary and exegesis, the influence of this newly expanded network of textual circulation can be seen in the sheer variety of sources Pei and Liu cite, as well as in their meticulous and unprecedented attention to bibliographic detail. This has made it possible to use their annotations to trace the compilers and titles of hundreds of texts that would otherwise be completely unknown. Relying on this wealth of information, earlier studies have tabulated the titles of all cited sources to create lengthy bibliographies. But in doing so, they divorce this bibliographic information from the context in which it was originally embedded. This study uses data mined from these annotations to create a series of network diagrams, which illustrate how citations of older texts create connections among the various chapters of the two texts to which they were appended. By considering the networks of textual relationships created through annotation, this study reveals the importance of otherwise marginalized texts in the construction of historiographic knowledge and sheds new light on how scholars of the early medieval period made use of, and made sense of, the increasingly vast sea of text to which they had access.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".