Spooking the System: Re-evaluating Belief Formation Through Psychological Disturbance and Supernatural Encounter in Early Modern English Drama
Bibliographic record
Abstract
This dissertation explores the early modern English stage as a significant site for producing, exploring, and meditating on psychological uncertainty, specifically through encounters between human characters and representations of the supernatural.I argue that the psychological disturbance these encounters engender allows for a re-evaluation of the process and nature of belief formation.Rather than dismiss dramatic supernatural representations as theatrical spectacles void of critical significance, I argue that the supernatural can be productive conduits for exploring the psychological complexities of the human mind.I demonstrate that because ghosts, devils and witches are products of the same theological and philosophical systems which conceptualize the workings of the early modern mind, they are able to infiltrate and influence the aspects of human psychology which are particularly susceptible to doubt and desire.These encounters then result in the psychological unravelling of human characters, who must navigate conflicting ideals in order to move towards action.However, the experience of this internal struggle is significant not only for revealing the competing influences characters must reconcile in the process of forming beliefs and opinions, but also for emphasizing the importance of critical evaluation of the discourses of belief that surround them, regardless of external pressure to blindly accept them.As ghosts, devils and witches are supernatural figures rooted in the religious, social and political ideologies that shape early modern English belief, so are they imbued with a powerful potential to shake the foundations of those belief systems of which they themselves are part.I analyze supernatural representations of these figures in plays by Shakespeare, Middleton, Marston, Chapman, Goffe and Marlowe, and the psychological effects of their encounters with human characters, in order to examine how they can facilitate alternative perceptions of beliefs around gender and power in sixteenth and seventeenth-century England.I would like to start off by thanking the wonderful faculty, staff and students of the Department of English, who have all contributed to creating such a warm, communal and collegial environment.I feel very lucky to have been part of this department for the last five years, as it has taught me so much about what it means to be a colleague, a teacher, a mentor, and an involved member of an academic community.I would like to extend thanks particularly to Dr. Julie Murray and Dr. Brian Johnson, who, in their roles as graduate supervisor, provided invaluable guidance and advice, not only about the degree program and its requirements, but about life in academia beyond the PhD.Thank you for always having an open door, a welcoming demeanor, and the patience to reassure me through the various bumps along the way.Thank you also to Lana Keon and Priya Kumar, the superb graduate administrators who always have all the answers, whose tireless efforts make life so much easier for the rest of us, and who are always available for a hug or a pep talk.I would also like to thank the internal members of my dissertation committee, Dr. Micheline White
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".