Creaturely pleasures : the representation of animals in early modern drama
Bibliographic record
Abstract
This dissertation addresses the profound influence that the shared experience of humans and animals had on the poetics of early modern drama. With reference to a selection of early modern comedies and a range of non-literary texts that includes natural history encyclopedias and animal husbandry manuals, I argue that the vernacular knowledge of animals shaped the early modern imagination generally and the early modern playwright's imagination particularly. I propose an original approach to early modern literature, one which urges integrating a consideration of the real-world referent for animal representation, the collective life lived by humans and animals, and the poetics of early modern drama. In my introduction, I take up the dissertation's general claims about the ethical and historiographical dimension of interpreting early modern animal representation. I continue to work at this theoretical level in Chapter One, where I consider how the animal-focused disciplines of sociobiology and ecology can help and hinder readers interpret early modern drama. In the following chapters, I work closely with a selection of early modern plays, contexts, and literary and theatrical devices. Chapter Two focuses on a web of comic plays that feature instantiations of animals in stage properties and actor's gestures. The web of plays in Chapter Two includes the anonymous Mucedorus; Lording Barry's Ram Alley; John Fletcher's Women Pleased; Thomas Nashe's Summer's Last Will and Testament ; William Rowley, Thomas Dekker, and John Ford's The Witch of Edmonton; Shakespeare's Love's Labor Lost; and Shakespeare and Fletcher's Two Noble Kinsmen. Chapter Three is devoted to the anthropomorphism of the allegorical representations of animals in Ben Jonson's plays Volpone and The Alchemist. In my reading of Shakespeare's A Midsummer Night's Dream in Chapter Four, I move on to consider animals whose representation is removed from reality not merely by anthropomorphism, but also by magic. All of these instances of representation draw animals into a sphere of existence that is commonly understood as the exclusive domain of humans at the same time that they draw humans in the other direction, which is to say into the muck and mire that is the origin of all life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.006 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".