Is Falstaff Falstaff? Is Prince Hal Henry V?: Topic Modeling Shakespeare’s Plays
Why this work is in the frame
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Bibliographic record
Abstract
This essay demonstrates how topic modeling can be fruitfully applied to TEI-encoded plays, which allows scholars to analyze speeches by individual characters. Our analysis centers on Shakespeare’s corpus and characters who reappear in multiple plays. Specifically, we use topic models to show that young Prince Hal (in <em>1 and 2 Henry IV</em>) does not speak the same language as his later self, Henry V (in his titular play): his linguistic shift mirrors his shift in status. Hal himself announces, “I have turned away my former self”—his change in diction bears out his assertion. Conversely, topic models reveal that Falstaff is Falstaff across multiple plays and genres (notably, <em>1 and 2 Henry IV</em> and <em>The Merry Wives of Windsor</em>), despite scholarly claims to that the Falstaff of comedy is a watered-down version of the braggart drunk of the history plays. Ultimately, we hope that this algorithmically-informed analysis of Shakespeare’s plays is not taken as a final answer, but, instead, as a prompt. As this research reveals, topic modeling plays with attention to each speaker opens the door for new comparisons, and in turn, expands on previous interpretations of literature. <hr /> Cet essai démontre que les modèles à thèmes (<em>topic model</em>) peuvent être appliqués avec succès à des pièces encodées en TEI, ce qui permet aux érudits d’analyser le discours de personnages individuels. Notre analyse se concentre sur le corpus de Shakespeare et sur ses personnages qui réapparaissent dans plusieurs pièces. Particulièrement, nous employons des modèles à thèmes pour montrer que le jeune Prince Hal (<em>1 et 2 Henri IV</em>) ne parle pas le même langage que celui qu’il parle après être devenu Henri V (<em>Henri V</em>): son changement linguistique reflète son changement de standing. Hal, lui-même, annonce: « j’ai renoncé à mon passé » —son changement de diction confirme cette affirmation. Inversement, les modèles à thèmes révèlent que Falstaff est Falstaff à travers plusieurs pièces et genres (notamment, <em>1 et 2 Henri IV</em> et <em>Les Joyeuses Commères de Windsor</em>), malgré des affirmations érudites que le Falstaff dans la comédie est une version édulcorée du vantard ivre des pièces d’histoire. Finalement, nous espérons que cette analyse algorithmique des pièces de Shakespeare n’est pas considérée comme une solution finale, mais plutôt comme une réplique. Comme cette recherche le montre, l’usage de modèles à thèmes pour analyser des pièces, ce qui se concentre sur chaque personnage, offre de nouvelles voies de comparaisons et étoffe donc nos interprétations de la littérature. <strong>Mots-clés:</strong> Shakespeare; pièces de théâtre; modèle thématique; modèles à thèmes; Falstaff; Prince Hal; <em>Henry V</em>; <em>Henri IV</em>; <em>Joyeuses commères de Windsor</em>
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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