“Whereunto I made them no answer, but smiled;” Textual and Ironic Authority in Anne Askew’s Examinations
Bibliographic record
Abstract
This essay examines the ways in which Anne Askew constructs an authoritative position for herself in her Examinations through the rhetorical placement of her smile. I focus specifically on how her smile acts as a response to her examiner’s questions and reverses the role of power from the male hierarchical position of authority to the subordinate female voice of Early Modern literature. Askew’s smile subverts the expectation of torture and imprisonment being a form of control over the body through her extensive biblical knowledge, use of Socratic irony as a form of response, and ability to manipulate her inferior hierarchical position as a woman and as an individual under interrogation. She carefully shifts the power dynamics throughout the text, using the smile to critique preconceived notions of masculine authority and manipulating the reader’s perception of herself by securing an authoritative position over her examiners. By allowing her readership a privileged understanding of her interrogations through the recordings of her accounts, Askew’s complicated text reveals the value of broadening the sphere of what counts as researchable texts within English departments, allowing a space to study non-traditional literature of Early Modern female authorship.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".