NEW VOICES IN EPIGRAPHY PAST AND PRESENT: UNPUBLISHED EPITAPHS FROM ROME
Bibliographic record
Abstract
The article aims to shed new light on the voices of bereaved benefactors: slaves, freedmen and freedwomen, who are often marginalized in literary and monumental sources, by exploring a series of unpublished funerary inscriptions from Rome, currently in storage at the Museo Nazionale Romano. Editions of the text, translations and commentaries have been produced by young scholars from the British School at Rome (former participants of the BSR Postgraduate Course in Epigraphy). Their entries, edited by Abigail Graham (Institute for Classical Studies, University of London, British School at Rome) and Silvia Orlandi (La Sapienza, President of the Association Internationale d’ Epigraphie Grecque et Latine), are an exciting and unique opportunity to view inscriptions through a different lens: from scholars with diverse backgrounds and interests (history, archaeology, epigraphy, as well as linguistics), including postgraduates and academics. Careful consideration of text, appearance and context presents an array of voices and audiences as well as poignant messages that transcend time and space through a common experience: grief. By incorporating interdisciplinary scholars in the editorial process, we aim to provide and promote uniquely accessible epigraphic discussions that reflect the broader impact and significance of epitaphs as texts, images and emotive experiences.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".