Representing Females from A Different Perspective: Justin Kurzel’s Film Appropriation of William Shakespeare’s Macbeth
Bibliographic record
Abstract
The study explores the differences in the representation of females, specifically Lady Macbeth and the witches, in both Shakespeare’s play, Macbeth (1623) and in Justin Kurzel’s film appropriation, Macbeth (2015). The paper attempts to prove that Kurzel’s film appropriation of Shakespeare’s play represents the female characters, specifically Lady Macbeth and the witches, from a different perspective than Shakespeare’s original paly. The paper shows how Kurzel changes the old ideas about how the witches and Lady Macbeth are the motivation and inspiration behind Macbeth’s downfall, defeat, and death. Instead, the study shows that Kurzel’s film appropriation posits that Macbeth’s greed and lust for power and authority make Macbeth himself the impetus behind his downfall, defeat, and death. As a result, Macbeth’s evil is natural rather than nurtured by the play’s main female characters as shown in Shakespeare’s original text. For this reason, Kurzel’s witches and Lady Macbeth are analyzed in relation to how they are portrayed in Shakespeare’s Macbeth. Very few studies tackle Kurzel’s film appropriation of Shakespeare’s Macbeth, which mainly focus on the lighting and visual effects of the movie. The contribution of the study is to fill the shortage of literature conducted on the current appropriation by Kurzel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".