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Record W4415276448 · doi:10.1017/s0068246225100317

NEW VOICES IN EPIGRAPHY PAST AND PRESENT: UNPUBLISHED EPITAPHS FROM ROME

2025· article· en· W4415276448 on OpenAlexaff
Abigail Graham, Silvia Orlandi, Chris Erdman, Katharina Korthaus, Benjamin Moon-Black, Victoria Muccilli, Alfredo Tosques

Bibliographic record

VenuePapers of the British School at Rome · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Religious Studies of Rome
Canadian institutionsYork University
Fundersnot available
KeywordsEpigraphyContext (archaeology)EmotiveSpace (punctuation)Scholarship

Abstract

fetched live from OpenAlex

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freedmen and freedwomen, who are often marginalized in literary and monumental sources, by exploring a series of unpublished funerary inscriptions from Rome, currently in storage at the Museo Nazionale Romano.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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.182
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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