The Concept of Villain in Shakespeare’s Othello
Bibliographic record
Abstract
The concept of evil has been researched since the Medieval era, leading to the conclusion that human beings have the freedom to choose good from bad, or evil from good. The origin of evil based on the religious teachings is Satan, who is described as the Rebel Angel, as explained by Dante in The Divine Comedy (Alighieri, 1957). Satan tempts human beings into sinning, as revenge against God for placing him in Hell. Based on the psychological point of view developed by Sigmund Freud, the source of evil is id which is distinctive (Freud, 1966). Villain motivations are driven by the tendency of the ego to make realistic decisions about meeting the unrealistic and unreasonable desires by the id. The other aspect that motivates villain actions include jealousy, anger and revenge, as indicated in the play. Shakespeare presents the villain character perfectly in his play Othello (1604) through Iago, whose main focus in life is to destroy others “So will I turn her virtue into pitch And out of her own goodness make the net That shall enmesh them all” (Shakespeare, 1993, p. 99). Through his manipulative skills, he makes the other characters trust him “Iago most honest” (Shakespeare, 1993, p. 75) and then fuel conflicts among them. Iago is motivated by anger, revenge and jealousy to commit the evil acts.
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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 | 0.001 |
| 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 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".