Bibliographic record
Abstract
Batman as Modern Revenge Tragedy: The Shakespearean Dark Knight Both Bob Kane’s Batman and Shakespeare’s Hamlet, lose parents, dress in black, put on antic dispositions, and go on personally motivated, violent missions for justice and revenge. The characters enter what seems to be an infinite mourning, where their innately flawed visions of obtaining justice are paired with unstoppable determinations. With my presentation, I intend on using video to demonstrate the historical trends of the revenge tragedy genre, and analyze their potential connection to the comic book medium, especially Batman. Using video, I can explore “adaptation” and all its nuances by implementing costume and visuals that mirror both the bard and the bat. Comics like The Dark Knight Returns (1986) and feature films like Batman V Superman, The Dark Knight Trilogy, and other Batman films act, in my view, as modern revenge tragedies; perhaps “Batman” becoming the entity that it is today has something to do with a legendary play, Hamlet, or at the very least, the legendary renaissance drama theatrics that might be hidden within it. Additionally, in light of Coen’s latest film, The Tragedy of Macbeth (2021), and Reeves’s The Batman (2022) release around the corner, themes of darkness, “dark knights,” and revenge continue to rivet contemporary consumers of the theatric and the cinematic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".