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Record W4383896113 · doi:10.33137/rr.v45i4.41380

Mala Vicinanza: Female Household-Heads and Proximity to Sex Work in Sixteenth-Century Florence

2023· article· en· W4383896113 on OpenAlexvenueno aff
Catherine Kerton-Johnson, Brandon Whitsit, Jennifer Mara DeSilva

Bibliographic record

VenueRenaissance and Reformation · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsResidenceSex workersLegislationFemale sexSex segregationCensusDemographySex workGender studiesGeographySociologyLawDemographic economicsPolitical scienceEconomicsMedicinePopulationResearch methodology

Abstract

fetched live from OpenAlex

In mid-sixteenth-century Florence the need to fund Santa Elisabetta delle Convertite, the convent sheltering retired sex workers, prompted the introduction of a higher tax on sex workers that offered freedom from identifying signs, geographic restrictions, and the title of meretrice. The result was precisely the diffusion of sex workers across the city that previous legislation has sought to avoid. While legislation identified sex workers’ mala vicinanza (evil proximity) as the justification for creating buffer zones around convents, conversely it also allowed sex workers to live within those buffer zones if they exhibited modestia e bontà (modesty and goodness). This unlikely loophole privileged Santa Elisabetta’s needs while allowing the segregation policy to fail. Using the 1561 decima census, this article tracks the residence of sex workers near to unenclosed female household-heads in an effort to explore the effect of Florentine magistrates’ ambivalence towards poor working women and the segregation policy’s failure.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.629

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.224
Teacher spread0.185 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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