Unsupervised Stylistic Segmentation of Poetry with Change Curves and Extrinsic Features
Why this work is in the frame
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Bibliographic record
Abstract
The identification of stylistic inconsistency is a challenging task relevant to a number of genres, including literature. In this work, we carry out stylistic segmentation of a well-known poem, The Waste Land by T.S. Eliot, which is traditionally analyzed in terms of numerous voices which appear throughout the text. Our method, adapted from work in topic segmentation and plagiarism detection, predicts breaks based on a curve of stylistic change which combines information from a diverse set of features, most notably co-occurrence in larger corpora via reduced-dimensionality vectors. We show that this extrinsic information is more useful than (within-text) distributional features. We achieve well above baseline performance on both artificial mixed-style texts and The Waste Land itself. 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it