“Farre More Then Himselfe”: Figures of Women’s Friendship in Early Modern England
Bibliographic record
Abstract
This dissertation uncovers how women become visible as friends in early modern literature through acts of imaginative invitation and poetic creation. My project intervenes in a critical discourse largely focused on men’s relationships to expand our understanding of friendship as a gendered phenomenon in the period, reading works across the genres of drama, romance, and poetry by both male and female authors. I explore the way that isolated female speakers take ownership of the ethical and performative layers of friendship, analyzing the strategies and figures they use to rework the utopian vision of friendship and define a space for themselves to speak.Historicizing texts by Shakespeare, Spenser, Wroth, and Whitney alongside other early modern sources published in England, I show how early modern women claim participation in the term “friend,” which signifies broadly across a spectrum that runs from the intimate commitments of ideal friendship to the utilitarian economic benefits of patronage. Each of these case studies confronts how the discourse of amicitia falls short for early modern women in its exclusionary masculinity and unrealistic idealism, and provides an amplified set of narratives for how friendship can be made. Rhetorical and literary performances are means for the exemplary female friends of my study to establish themselves as subjects in friendship—once “authors,” so also “friends.” The first part of my dissertation tracks how memory and imagination offer modes of transforming the present: Emilia (The Two Noble Kinsmen) and Celia (As You Like It) shape fresh realizations of friendship by recalling distant pasts, while Britomart and Amoret (The Faerie Queene) imagine an alternate future to transform discord to friendship. The second part examines poetry that acts as a placeholder in friendship’s absence and articulates its authors’ need for real companionship: Urania and Pamphilia (Urania) invoke echoic voices from the natural world, while Isabella Whitney (A sweet Nosgay) invites readers to share her relationship with her garden-text. These case studies illustrate how early modern women might claim the discourse of friendship, revealing dynamic models of female friendship that go beyond the masculine mythologies of amicitia.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".