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Record W4405180685 · doi:10.33137/rr.v47i3.44463

Wives, Widows, Daughters, and Guildswomen: Female Workers in Sixteenth-Century Florentine Taverns and Inns

2024· article· en· W4405180685 on OpenAlexafffundvenue
Jennifer Mara DeSilva, Samuel Didone, Qing‐Ling Ni

Bibliographic record

VenueRenaissance and Reformation · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Toronto
FundersFaculty of Education, Victoria University of WellingtonUniversity of TorontoUniversity of OxfordJohns Hopkins UniversityYale University
KeywordsGender studiesMoralityCensusSociologySpace (punctuation)Political scienceDemographyLaw

Abstract

fetched live from OpenAlex

Early modern Florentines depicted their taverns as wonderful and raucous places that were central to masculine sociability and a threat to female chastity. Many modern historians have followed this lead, presenting the history of Florentine taverns as a study of contemporary sociability and morality. In contrast to the male-centric tavern, this study explores sixteenth-century Florentine taverns as places of work employing many women in a variety of roles. Focusing on workscapes problematizes occupational gendering and considers how taverns involved the tavern keeper’s household and neighbours. Using guild, census, and judicial records, this study explores the spaces that some female tavern keepers, procuresses, and sex workers shared in an effort to reveal women’s work in a professional space previously considered to be exclusively male-dominated.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.008
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.211
Teacher spread0.194 · 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 designObservational
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
Published2024
Admission routes3
Has abstractyes

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