De-‘Moor’ Tifying Shakespeare’s Othello: Iago as a Renaissance Form of Islamophobia
Bibliographic record
Abstract
Albeit Othello’s loyal service to Venice is clear, ramifications of the Blackamoor in the text has called upon a continuous scholarship of interest over the last two decades. One begins to wonder, whether the Blackamoor’s presence in a Venetian setting serves a purpose to the English readership? If so, to what extent did Shakespeare refine the representation of a Moor to draw upon readership? How does Iago serve as a propagandist who articulates and fuels concerns for the seventeenth-century version of Islamophobia? As such, these significant questions have led to the construction of this paper, since these issues continuously problematize and make relevance of Shakespearean plays in the contemporary world. As such, this article examines the significance of Shakespeare’s inclusion, portrayal and representation of Othello as a Moor that poses an image of threat in a seventeenth-century Western context. At the same time, this paper also asserts that Iago is the mouthpiece who initiates worries over possible threats of Islamophobia. The use of Greenblatt’s New Historicism, particularly his concept of energia (1988) enables the validation of such claims by making relevance of the Battle of Lepanto (1571). Results indicated that ‘Othello’ provides an important platform to discuss contemporary renaissance issues such as Islamophobia and Englishness, through indicative clues that Iago is the mastermind that causes fear of the advancing Moors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".