Value Assessment and Linguistic Feature Mining Based on Regression Analysis in Ancient Literary Texts
Bibliographic record
Abstract
The value assessment of ancient literary texts and the mining of linguistic features are indispensable parts of academic research and ancient cultural inheritance.This paper uses the multiple regression model as a quantitative analysis tool for value assessment to evaluate the value of ancient literary texts.At the same time, for the linguistic features of ancient literary texts, we put forward the quantitative descriptive definitions of words, phrases, sentences and other multi-layer and multi-latitude, and establish the corresponding calculation formulas.After the assessment of the value of ancient literary texts, it can be learned that, except for the artistic law and the breadth of dissemination, the ancient literary texts are positively correlated with other influencing factors such as the writing method and the rhythm and rhyme, and the gap between the predicted value of the value assessment and the real value is small, with an error of 40% or less in 90% of the cases.In the mining analysis of linguistic features using The Peony Pavilion and The West Wing as research objects, the average word length of the former is slightly higher than that of the latter, while the difference in the distribution of long and short sentences of the latter is relatively large.Meanwhile, the average dependency distance of The Peony Pavilion is 2.42, which is higher than that of The Story of the Western Wing by 0.1, making syntactic analysis more difficult.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".