«Han canonizado a cuatro espanoles y un santo» : la propuesta hagiografica del oratoriano Felipe Neri entre «el esplendor de Iberia» y «la gloriosa memoria de Enrique 4.»
Bibliographic record
Abstract
Nel 1622 Gregorio XV canonizzò Isidro Labrador, Teresa d’Avila, Ignacio de Loyola, Francisco Javier e Filippo Neri. L’articolo si concentra sulla storia del processo in onore del fondatore degli oratoriani e sui sostegni che la causa ricevette dalla Toscana e dalla Francia. Inoltre, mette in risalto come una serie di decisioni (la canonizzazione multipla, l’ordine di precedenza dei santi, la bolla pontificia, gli apparati per la festa) dipesero da una contrattazione politica e diplomatica portata avanti dal papa con l’obiettivo di trovare un punto di equilibrio tra la Francia e la Spagna e, soprattutto, di fare risaltare il carattere “romano” di quelle canonizzazioni.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".