Emotion and the Humors: Scoring and Classifying Major Characters from Shakespeare's Comedies on the Basis of Their Language
Bibliographic record
Abstract
The theory of humors, which was the prevalent theory of affect in Shakespeare's day, was used to explain both states (moods, emotions) and traits (personalities). This article reports humoral scores appropriate to the major characters of Shakespeare's comedies. The Dictionary of Affect in Language was used to score all words (N = 180,243) spoken by 105 major characters in 13 comedies in terms of their emotional undertones. These were translated into humoral scores. Translation was possible because emotional undertones, humor, and personality (e.g., Eysenck's model) are defined by various axes in the same two-dimensional space. Humoral scores differed for different types of characters, e.g., Shakespeare's lovers used more Sanguine language and his clowns more Melancholy language than other characters. A study of Kate and Petruchio from The Taming of the Shrew demonstrated state-like changes in humor for characters as the play unfolded.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".