Bibliographic record
Abstract
There seems to be an insatiable demand for biographies of early Roman emperors, with Nero unquestionably heading the list. The last decade or so has seen Miriam T. Griffin's authoritative Nero: the End of a Dynasty (1985, reprinted 2001), Edward Champlin's individualistic and often inspired Nero (2003) and, for the general reader, Jürgen Malitz's Nero (2005) and David Shotter's own Nero (1997, reprinted 2005) in the “Lancaster Pamphlets” series. Is there a case for yet another book on the same emperor? Shotter's new volume does indeed meet a clearly identifiable need. It is aimed at a non-specialist readership, but unlike Malitz's book and Shotter's own earlier biography, both in series that dictated summary treatments, this volume recognizes an essential truth about the classical world: that there are very few topics not vexed by scholarly disputes and that there are few undisputed “facts.” This holds especially true for a colorful figure like Nero. Commendably, Shotter here provides non-specialist readers with proper notes and references, as well as with extensive bibliographical information, essential tools for the proper understanding of the contentious issues raised. While the label “popularizer” might be attached to him, it carries no stigma, since Shotter underpins this role with a raft of published scholarship on early imperial history.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".