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Record W1979708244 · doi:10.1163/15700658-12342326

She Said, He Said: Situated Oralities in Judicial Records from Early Modern Rome

2012· article· en· W1979708244 on OpenAlexaff
Elizabeth S. Cohen

Bibliographic record

VenueJournal of Early Modern History · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Influence and Diplomacy
Canadian institutionsYork University
Fundersnot available
KeywordsOralitySituatedAgency (philosophy)HistoryExpression (computer science)LiteratureSociologyArtLiteracyLawPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Abstract In practice, early modern culture was for most Europeans more oral than written. Yet spoken words, especially those of ordinary people, are, for scholars, tantalizingly elusive. Testimonies, recorded verbatim, in judicial proceedings for the city of Rome and other Italian jurisdictions offer rich repositories of oral expression uttered by women and men of diverse ages and social positions. Yet to explore these documents as terrains of speech and oral culture, we must attend closely to the processes by which these words were assembled and transcribed. Everyday talk that we hear in the trials was deeply situated: in the intricate hybridity of oral/written cultures that characterized much of the early modern world; in the layered oral and written formats of judicial process; and in the social and gendered circumstances of the speakers. These frames shaped the orality that we see in the trials, but did not obliterate individual agency in speech.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0050.008
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.060
GPT teacher head0.304
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2012
Admission routes1
Has abstractyes

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