A study of the Shi Jing (The Book of Odes) manuscript excavated at Fuyang
Bibliographic record
Abstract
In a general sense, this thesis examines several aspects of the Shijing [Chinese characters] (The Book of Odes) manuscript excavated at Fuyang [Chinese characters], Anhui province, the People's Republic of China. The introductory chapter provides background on the Fuyang Shi jing, as well as the transmitted versions of this Chinese classic. Further, it outlines the traditional scholarship dealing with the Shi jing and offers a detailed summary of the other chapters. Chapter one introduces the methodology used in chapters two and three of this thesis. Part one of this chapter demonstrates the manner in which lexical and graphic variation is defined and analyzed, while part two shows how graphs may be analyzed based on a clear understanding of orthographic feature. Chapter one also provides background on the history of early Chinese writing forms and the evolution of Chinese script. Chapter two is primarily an orthographic study with two main objectives: (A) to distinguish the physical nature of the Fuyang Shi jing; and (B) to date this manuscript. In the introduction to this chapter, I will also compare the Fuyang Shi jing with other early bamboo and silk manuscripts. Chapter three deals with lexical variation involving the Fuyang Shi jing and the various transmitted Shi jing texts. It has three objectives: (A) to provide new readings of several odes in the Shi jing; (B) to survey different types of lexical variation associated with the Fuyang manuscript; and (C) in each case determine which lexical variant, among the various possibilities, most likely represents the proximate original of the Shi jing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".