Les relacions veïnals entre cristians i jueus a Cervera durant el primer quart del segle xv
Bibliographic record
Abstract
catalaEn aquest article comentem les relacions veinals existents entre la majoria cristiana i la minoria jueva —un 10 % de la poblacio— a la vila de Cervera durant el primer quart del segle xv. Aquests comentaris els hem agrupat en cinc apartats: la separacio de les dues comunitats dins l’espai urba, l’atraccio de nous veins jueus, la defensa dels jueus davant terceres persones, el compliment de les normes per part dels jueus i el treball que alguns jueus feren per a la Paeria. Adjuntem la bibliografia de les obres citades i la transcripcio dels cinquanta-vuit documents comentats. EnglishThis article looks at the relations as neighbours between Cervera’s Christian majority and Jewish minority (approximately 10% of the town’s population) in the first quarter of the 15th century. The observations made are organised into five sections: the separation of the two communities within the town; how new Jewish residents were attracted; the protection of the Jews against third parties; the Jews’ adherence to the rules; and the work some Jews did for the municipal authorities. The article comes with a bibliography of cited works and transcriptions of the 58 documents referred to.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".