<i>Empire of Letters: Writing in Roman Literature and Thought from Lucretius to Ovid</i> . By <scp>Stephanie Ann Frampton</scp> <i>Empire of Letters: Writing in Roman Literature and Thought from Lucretius to Ovid</i> . By FramptonStephanie Ann. Oxford: Oxford University Press. 2019. xii + 206 pp. £47.99. <scp>isbn</scp> 978 0 19 091540 7.
Bibliographic record
Abstract
Since William V. Harris'sAncient Literacy (Cambridge, Mass., 1989) students of the book during the classical period have been struggling with the question of how widely Greeks and Romans wrote and read. Harris's argument suggested a large proportion couldn't read at all, and his position seemed strong, given the dearth of physical evidence that forced us to rely for the history of writing and reading in the period on allusions in canonical literary works and sometimes the iconographic legacy of the time. In the ensuing three decades the Harris thesis has been disputed by a number of scholars (many of them drawn on in this new book) who have turned to broader concepts of available evidence, implicitly repudiating the hierarchies in terms of which the classical canon—Homer, Virgil, Ovid—was perceived. Not that the authors themselves were repudiated, as Stephanie Ann Frampton's study shows, but that the scene has been widened...
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".