Bibliographic record
Abstract
A. Foakes aims in Shakespeare and Violence to chart the development of Shakespeare's thinking about violence over the course of his dramatic career. Foakes argues that Shakespeare's plays "may be seen as following a trajectory that begins with a delight in representing violence for entertainment, continues in a series of plays that explore various aspects of the problem of violence, and ends with a searching study of human aggression in relation to self-control in The Tempest" (p. 2). Adopting Ren Girard's model of violence as a means of creating status differences between rivalrous but otherwise identical males, the book's fundamental assumption is that human beings (or at least human males) are naturally violent and express their aggressiveness in remarkably similar ways in spite of other cultural differences. Culture in this model is a progressively ameliorating but rather fragile force. The book's Girardian approach produces interesting but at times strained readings of male violence in a range of Shakespeare's plays. Foakes reads the plays in the context of Christian and classical myths of violence, such as the Cain and Abel story and Homeric myth, often in conjunction with analyses of the plays' film adaptations intended to demonstrate that "although Shakespeare's world was very different from that of the present day . . . the basic issues remain the same" (ibid.). Foakes's readings are wide-ranging and thought-provoking, but the reader may find their tendency to elide cultural difference problematic and may regret Foakes's decision -explicitly stated at the book's outset -not to address the role of violence in the production of gender distinctions.
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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 | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".