Romans à l’encan. De l’art du boniment dans la littérature au XVIe siècle
Bibliographic record
Abstract
comptes rendus 123 of plague and social unrest.The statement that the brothels of Southwark were "owned" by a former lord mayor in the fourteenth century and under government regulation until 1506 (32) seems a casual distortion of a more complex situation (the prostitutes of Bankside were not called the "bishop of Winchester's geese" for no reason).Yet, despite some imprecision and flaws in interpretation, Bayer's goal to redress the balance in perception of suburban theatres is welcome.Not only the stimulus for disorder and subversion as represented by E.K. Chambers, Stephen Mullaney and others-though sometimes they undoubtedly were-the early modern theatres of London also became important social institutions contributing to local economy, popular education, welfare, and community cohesion as well as entertainment in a period of religious turmoil, recurring plague, and dramatic population growth.It is a compliment to Bayer's work that the reader emerges with a clear sense that more work with a similar focus on social networks in a geographic context would be welcome for other early theatres in Middlesex and Southwark.His book is therefore the first, but not the last word on the theatres, communities, and civic engagement in early modern London and its suburbs.sally-beth maclean, Records of Early English Drama Bayle, Ariane. Romans à l' encan. De l' art du boniment dans la littérature au
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".