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Record W3202038214 · doi:10.1163/15700658-bja10021

Who Owned Florence?: Religious Institutions and Property Ownership in the Early Modern City

2021· article· en· W3202038214 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Early Modern History · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProperty (philosophy)CensusOrder (exchange)ContemplationPower (physics)HistoryBusinessSociologyPolitical scienceGeographyDemographyFinanceTheologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract This study employs a 1561 tax census to survey estimated property incomes in Florence with particular attention to lay and ecclesiastical religious institutions. Its key findings are five. First, religious institutions were collectively the wealthiest corporate entities in the city, holding one fifth of all residential properties and one third of all workshops, and drawing 20.2 percent of all property income generated within city walls. Second, many were civic- and lay-religious institutions such as confraternities and hospitals. Third, the property income of religious houses was distributed across multiple organizations while that held by the Florentine diocese was concentrated in a few. Fourth, among religious orders, Mendicant houses had a larger urban presence than the older contemplative houses. Fifth, the property holdings of the formally defunct military-religious order of the Knights of S. Jacopo signal the deftness with which some institutions adapted to new circumstances. Overall, this survey of property incomes helps quantify the shape of power in the Florentine religious universe.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

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

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 head0.098
GPT teacher head0.230
Teacher spread0.131 · 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