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Record W2595318807 · doi:10.33137/rr.v35i3.19523

Converting Goods into Cash: An Ethical Approach to Pawnbroking in Early Modern Bologna

2013· article· fr· W2595318807 on OpenAlexvenueno aff
Mauro Carboni

Bibliographic record

VenueRenaissance and Reformation · 2013
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

L’émergence des Monti di Pietà dans les villes italiennes des débuts de la modernité a joué un rôle important en permettant une grande circulation des liquidités dans les portions moins nanties du marché. À travers le prêt sur gage, le Monte offrait la possibilité de transformer temporairement en argent liquide les petites richesses non-monétaires, à divers degrés de l’échelle sociale. Le Monte a ainsi contribué à l’expansion du crédit, et exercé une importante fonction anticyclique dans les économies locales. En puisant dans les archives d’une de ces institutions les plus prospères — le Monte de Bologne —, cet article explore l’étendue de ce phénomène, l’importance des montants en jeu, la variété des biens mis en gage, ainsi que l’impressionnante variété des clients. Au sommet des activités du Monte, ses clients n’appartenaient plus seulement à la classe des travailleurs pauvres. Toutefois, ce changement n’a pas nuit à l’accessibilité du crédit aux classes pauvres, mais a plutôt contribué à ce que ces services soit offerts pour moins cher à ceux véritablement dans le besoin.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.028
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0020.002
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 head0.035
GPT teacher head0.240
Teacher spread0.205 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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