Bibliographic record
Abstract
This volume is a tribute to John Beattie, whose work is fundamental to the burgeoning study of crime and the courts in early modern England, and whose enthusiastic interest in the work of his fellow historians is one of the attractions of eighteenth-century English history. On his retirement, John's current students and colleagues at the University of Toronto published a Festschrift in his honour. This is therefore the second volume dedicated to John. Of the contributors to this volume, some were John's students as undergraduates, others his graduate students, and all enjoy his friendship. John is an extraordinary scholar: not only acute, persistent, and insightful in his own work, but generous in giving his time, advice, and aid to others. John's work has made our work better; his presence has enhanced our enjoyment of our work. This volume is one way in which we say ‘thank you’. The chapters in this volume develop themes raised by John Beattie's second and third books, Crime and the courts in England, 1660–1800 and Policing and punishment in London, 1660–1750 . The foundation of both books is analysis of the charges of felonious conduct brought before Quarter Sessions, Assizes, and the Old Bailey (London and Middlesex's Assizes), and the way in which these courts dealt with these allegations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.516 | 0.347 |
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".