Bibliographic record
Abstract
This book began with a question asked by students in a seminar about Women in the Renaissance. “Why were servants so prominent?” they wondered, reflecting on the anonymous tragedy Arden of Feversham and on the poems of Isabella Whitney. They belonged, as does much of the book, to an era of scholarship which encouraged close scrutiny of subordinate actions. Without the enthusiastic support of students and colleagues at the University of Manitoba, I could never have developed this project: Judith Owens criticized papers and shared research discoveries; John Rempel sent reading lists; George Toles made me take psychological criticism more seriously. For challenging arguments and indispensable information I would also like to thank Adam Muller, Karen Ogden, Arlene Young, Terry Ogden, Jonah Corne, Kathleen Darlington, and Nicola Woolff. Sociologists Charlene Thacker and Raymond Currie introduced me to connections between service and slavery by suggesting studies on Brazil and South Africa. I am grateful to the Shakespeare Association of America for inviting me to chair a seminar on “Slavery in Renaissance Drama”; strong contributions by seminar members opened up a number of exciting new perspectives. I am also much in debt to Lynne Magnusson and Edward McGee for asking me to present a paper to the Elizabethan Theatre Conference exploring my approach. At a crucial early stage, criticism of the project by Scott Macmillin made it seem more promising than I had thought possible.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.426 | 0.233 |
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".