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Record W1987042290 · doi:10.1093/ahr/119.2.626

Nicholas Terpstra. Cultures of Charity: Women, Politics, and the Reform of Poor Relief in Renaissance Italy.

2014· article· en· W1987042290 on OpenAlexaff
Mark Jurdjevic

Bibliographic record

VenueThe American Historical Review · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsPoliticsContext (archaeology)InstitutionPoor reliefDecreeThe RenaissanceOperaSociologyClassicsHistoryLawPolitical scienceArt historyArchaeologyPoverty

Abstract

fetched live from OpenAlex

In Cultures of Charity: Women, Politics, and the Reform of Poor Relief in Renaissance Italy, Nicholas Terpstra provides a kaleidoscopic analysis of the varieties of poor relief in sixteenth- and seventeenth-century Bologna. For much of the Renaissance, the Bolognese charitable context was a sprawling decentralized system subject to frequent change, hence Terpstra's consistent emphasis on the plurality of models adopted by the Bolognesi. He ties together the many threads in the book's survey by focusing on one confraternal institution in particular, the Opera Pia dei Poveri Mendicanti (OPM), created by papal decree in 1560. Although Terpstra makes occasional comparative forays into charitable practices in Florence, Genoa, Lyons, and Seville, his analysis is squarely focused on the OPM as informal hub and coordinator for a host of Bologna's smaller charitable services and activities. All but one chapter follow the OPM's activities through the turbulent Bolognese politics of the later sixteenth and seventeenth centuries, showing how its merchant and artisan members, many of whom had close ties to Bologna's senate and governing families, modified poor relief in ways that reflected the tensions and controversies of a rapidly changing social, economic, and political context.

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)0.001
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: none
Teacher disagreement score0.843
Threshold uncertainty score0.985

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 studies0.0010.002
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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
Published2014
Admission routes1
Has abstractyes

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