Quantifying Genre: An Operational Definition of Tragedy and Comedy Based on Shakespeare's Plays
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
Emotion and imagery in the words of Shakespeare's plays, as measured by the Dictionary of Affect in Language, were used to predict genre (tragedy or comedy). Genre distinctions, which were associated with small effect sizes, were established on the basis of 23 plays and then applied to other plays. A discriminant function which combined lower emotional Pleasantness with higher emotional Activation or arousal and more pictorial Imagery successfully (91% of the time) predicted whether a play was a tragedy or a comedy. The genre-discriminating formula provided meaningful categorizations of 23 additional plays. As hypothesized, comedies employed more Pleasant words than tragedies. Tragedies employed more Active words (p < .001). Unexpectedly, comedies rather than tragedies employed words with lower Imagery (greater Abstraction). The predicted elevation of language in tragedy was noted instead in the use of more verse, fewer common words, and fewer personal pronouns (less subjectivity).
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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 | 0.002 | 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