The Astrological Texts of King Alfonso X of Spain, and His Contribution to Modern Science
Bibliographic record
Abstract
In modern Western society the study of astrology is not taken seriously by the majority of people. However, in the medieval period astrology was held to be a hard science. Astrology was studied in the East long before it came to be pursued by Western scholars. The 8th century A.D. Moslem invasion of Spain meant that much of this knowledge was brought to Europe. One of the key figures in the reacquisition of this knowledge was King Alfonso X, known as El Sabio, or The Wise, for his love of learning. Shortly after his coronation he established groups of translators at his court in Toledo who were responsible for translating texts from their original language into Spanish. The majority of the works he ordered to be translated were Eastern texts on the sciences, the majority of Arabic origin. In particular he had a great interest in the study of astrology, and included many Eastern astrological texts in his library. In addition to translation Alfonso occasionally expanded upon the work of the Eastern scholars, contributing his own observations to theirs. In this paper I give an overview of the contributions Alfonso made to the study of astrology, with particular focus placed upon two of his works: Lapidario, and Libro de formas e ymagenes, both of which study the relationship between astrology and stones. These texts demonstrate scientific experimentation and analysis in its earliest stages and by studying these documents, the role of Alfonso X in the development of modern Western science can clearly be seen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".