Computational Stylistics in Poetry, Prose, and Drama
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This volume responds to the current interest in computational and statistical methods to describe and analyse metre, style, and poeticity, particularly insofar as they can open up new research perspectives in literature, linguistics, and literary history. The contributions are representative of the diversity of approaches, methods, and goals of a thriving research community. Although most papers focus on written poetry, including computer-generated poetry, the volume also features analyses of spoken poetry, narrative prose, and drama. The contributions employ a variety of methods and techniques ranging from motif analysis, network analysis, machine learning, and Natural Language Processing. The volume pays particular attention to annotation, one of the most basic practices in computational stylistics. This contribution to the growing, dynamic field of digital literary studies will be useful to both students and scholars looking for an overview of current trends, relevant methods, and possible results, at a crucial moment in the development of novel approaches, when one needs to keep in mind the qualitative, hermeneutical benefit made possible by such quantitative efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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