Criticism of Narrative Ethics in Chinese New Century Literature: a Cluster Analysis of AI Perspectives
Bibliographic record
Abstract
From the perspective of artificial intelligence (AI), this paper explores the application and impact of cluster analysis in the criticism of narrative ethics in Chinese new century literature. Utilizing AI paper processing technology, a large amount of literary text data is quickly obtained and processed, and a knowledge map of narrative literary works is constructed. Meanwhile, a clustering algorithm is used to divide the keywords of literary works into cluster classes to improve the efficiency of rapid literary analysis. The regression model is used to evaluate the effect of the cluster analysis method in the AI perspective on the ethical criticism of literary narratives. The accuracy, recall, and F1 value of the two AI techniques selected in this paper in the classification of literary text themes, keywords, and emotions are 85% to 90%, which is higher than the comparison methods, and combined with the clustering algorithm, the keyword categories of the literary text can be obtained quickly and precisely. In addition, by constructing a knowledge graph, this paper can help users grasp the character relationships in literary texts more clearly and assist in ethical criticism. The investigators are highly satisfied with the method of this paper, the average rating of each dimension is between 4.09 and 4.7, and the method has a significant contribution to the effect of ethical criticism of literary narratives.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".