Shakespeare's Telling Words: Grammar, Linguistic Encounters, and the Risks of Speech
Bibliographic record
Abstract
This dissertation analyzes undertheorized grammatical and linguistic details of Shakespeare’s language. Using tools derived from the fields of linguistics, pragmatics, and discourse analysis, I trace the ways that Shakespeare’s speakers represent themselves in language, and how they position themselves relative to their interlocutors. Grounding my study in a selection of Shakespeare’s works in which questions of self-positioning are particularly fraught, I argue that the nuances of grammar that undergird the linguistic performance of Shakespeare’s speakers encode significant clues about interaction and interpersonal relationships. I maintain that the minute details of linguistic encounters, easily overlooked words such as modal verbs (particularly shall and will) and deictic markers (words such as I, this, and now), hold important information about speakers’ perceptions of themselves, their interlocutors, and their environment. Attention to such details, and to charged moments of linguistic encounter in which speakers must negotiate their modes of self-positioning, helps to illuminate the troubled processes of self-representation and changing self-perception. Chapter one focuses on Shakespeare’s sonnets, and suggests that these poems provide a productive model for the examination of the nuances of speech and interactive dialogue. I anchor my discussion in the particular resonance of the word shall in the sonnets, and explore the ways in which the sonnet speaker attempts to preserve linguistic control relative to a threatening interlocutor. The second chapter extends these concerns to consider how the speakers of Troilus and Cressida respond to a wide network of potentially threatening interlocutors. In this chapter, I focus on linguistic encounters such as arguments and gossip to examine the risks that speakers encounter when they enter the fray of communal discourse. My third chapter turns to Coriolanus to consider moments of aggressive linguistic collisions, in which speakers vie for the right to speak a potent and contested word such as shall. The fourth and final chapter analyzes Richard II through the frame of deictic markers and grammatical modes of self-reference to consider the protective strategies afforded by language in moments of crisis.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".