Bibliographic record
Abstract
Few buildings have played so central a role in Spain's history as the monastery-palace of San Lorenzo del Escorial. Colossal in size and imposing-even forbidding-in appearance, the Escorial has invited and defied description for four centuries. Part palace, part monastery, part mausoleum, it has also served as a shrine, a school, a repository for thousands of relics, and one of the greatest libraries of its time. Constructed over the course of more than twenty years, the Escorial challenged and provoked, becoming for some a symbol of superstition and oppression, for others a wonder of the world. Now a World Heritage Site, it is visited by thousands of travelers every year. In this intriguing study, Henry Kamen looks at the circumstances that brought the young Philip II to commission construction of the Escorial in 1563. He explores Philip's motivation, the influence of his travels, the meaning of the design, and its place in Spanish culture. It represents a highly engaging narrative of the high point of Spanish imperial dominance, in which contemporary preoccupations with art, religion, and power are analyzed in the context of this remarkable building.
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 | 0.001 |
| 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 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".