From the Closet to the Wallet: Pawning Clothes in Renaissance Italy
Bibliographic record
Abstract
Dans l’Italie de la Renaissance, ce sont les vêtements qui sont le plus couramment mis en gage par ceux qui cherchent à obtenir des prêts auprès des banquiers juifs et du Monte di Pietà. Des robes, des chemises et même des chaussures sont mis en gage, et les vêtements féminins le sont plus souvent que les vêtements masculins. Cet article examine les divers types de vêtements que les emprunteurs — hommes et femmes — offraient de mettre en gage, dans le but de déterminer leur qualité et leur valeur, et de cerner ainsi l’identité de ces clients. En prenant appui sur cette thématique, cet article montre comment l’analyse des changements de types de vêtements mis en gage à la fin du XVe et au XVIe siècle peut nous aider à mieux comprendre les changements de motivations et d’identités des emprunteurs de cette époque.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".