Haunting Emotions: Visualizing Hamlet's Melancholy for Students in Two Recent Graphic Novel Adaptations
Bibliographic record
Abstract
The study of emotion and Shakespeare and, in particular, emotion and Hamlet, is well established. Shakespeare's work enables us to experience emotions and their transformations as we try to understand them. From the opening of the play, Hamlet's emotions are all too clearly present; Shakespeare defines him as a passionate and emotional man plagued by melancholy. How is this human emotion interpreted and visualized by authors attempting to adapt Hamlet in the twenty-first century? In recent years, visual literacy has become a prominent aspect of classroom learning. In a changing, more visually dependent world, students need to learn how to read the visual as well as the textual. The medium of graphic storytelling can help students learn how to do this. This paper will examine two recent graphic novel versions of Shakespeare: Kill Shakespeare (2010-current), by Canadian writers Anthony Del Col and Conor McCreery (alongside Andy Belanger as head-artist), and Australian author Nicki Greenberg's Hamlet (2010). Each of these graphic novels includes the character Hamlet as the protagonist, and each of these texts approaches adapting the melancholy Dane (and Shakespeare's "text") in very different ways. Through comparisons with Shakespeare's canonical play-text, including Shakespeare's incorporation of humoural ideas of melancholy, we will analyze how this aspect of Hamlet's emotions are visually interpreted and developed in these two new media adaptations. The essay concludes that these adaptations of Hamlet work well as a text for K-12 students because the emotions Hamlet experiences are presented in a relatable way. The texts help these students to understand the emotions, and so relate to a character whose complex personality may otherwise be lost in the difficulty of the original text.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".