Bibliographic record
Abstract
Her mono graph (Dramatic Extracts in Seventeenth-Century English Manu scripts: Watching, Reading, Changing Plays, 2015), co-edited collections (Early Modern Studies after the Digital Turn, 2016 and Early British Drama in Manu script, 2019), and numerous articles speak to her interest in the reception of early modern drama from its initial manu script circulation to digital representations today.early modern CommonPlaCe books and miscellanies, those important artifacts of literary and textual culture, reflect historical tastes, attitudes, and learning practices.1 This chapter uses the broad definition of commonplace book as a volume consisting primarily of commonplaces, that is, "well-phrased sayings that express a pearl of wisdom." 2 For centuries, readers copied passages into their notebooks: some, commonplace books, filled primarily with textual excerpts and commonplaces; others, miscellanies, filled with receipts (recipes), poems, and other textual bits and bobs.As Eric Rasmussen and Ian H. De Jong explain, "Commonplace books are rich with historical evidence, shedding light on individual readers' habits * I'd like to thank Tara Lyons and Constance Crompton for their thoughtful suggestions on this chapter.Thanks also to John Heggelund, Bethany Radcliff, and the students in Texas A&M ENGL303 (Spring 2017) and ENGL617 (Spring 2017) for engaging pedagogical experiences. 1 For more on the importance of commonplace books and their role in understanding literary history, see Ann Moss, Printed Commonplace-Books and the Structuring of Renaissance Thought (Oxford: Clarendon, 1996); Peter Beal, "'Notions in Garrison'
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".