How Well Did the Commentators Reverse-Engineer the Zhouyi? A Pre-registered Test of Two Interpretive Rules Against the Received Text
Year: 2026
Type: preprint
Abstract: The line statements of the Zhouyi were compiled in the Western Zhou. The interpretive apparatus most often used to read them — centrality (zhong), correctness (zheng), correspondence (ying), and the adjacency relations cheng (supporting) and cheng (riding) — is set out in the Ten Wings, five centuries later. That apparatus therefore cannot have generated the line statements. It is a reverse-engineering of a text that already existed. This paper asks how good that reverse-engineering was, and treats the question as measurable rather than interpretive. We pre-registered two rules and hashed all 384 predictions before obtaining any line-statement text. Rule A uses only the twelve-character positional maxim of the Xici: the second line often brings praise, the third often misfortune, the fourth often apprehension, the fifth often merit. Rule B uses the full zhong-zheng-ying-bi apparatus. Judgments were assigned to the 384 line statements by a published deterministic keyword rule, not by human reading. On the 254 line statements that carry an explicit favourable or unfavourable judgment, Rule A is correct 66.9% of the time and Rule B 65.4%, against a majority-class baseline of 59.1%. On the 87 items both rules judge, Rule B is nominally higher (75.9% against 72.4%) but the difference is not significant (McNemar chi-square = 1.33). By the pre-registered criterion, the fuller apparatus shows no explanatory power independent of the positional maxim. We report the number, the corpus, the annotation rule, and a construct-validity problem that our own pre-registration failed to anticipate. An appendix records the formulations we tried and rejected, since each corresponds to a question a reader is likely to ask.
Author: Hu, Xiangyu
Language: English
Subfield: Literature and Literary Theory
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7203705980
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