To Preserve and Protect: Husayn Vaᶜiz-i Kashifi and Perso-Islamic Chancellery Culture
Bibliographic record
Abstract
It is Generally Agreed that the Beginning of the Persian Literary Renaissance of the tenth–eleventh centuries was concomitant with the emergence of a bureaucratic tradition that had its roots in pre-Islamic Sasanian Iran. The avatars of these bureaucratic principles in medieval Perso-Islamic history—Nizam al-Mulk, Rashid al-Din, Nasir al-Din al-Tusi, to name the most famous—were trained in Arabic and Persian, wrote scholarly treatises on a wide array of religious, philosophical, and scientific topics, and advocated, among other things, the need for responsible rule and consistent administration according to Islamic law and custom. These admonitions were generally directed at their Turkic and Mongol dynastic patrons whose steppe traditions were often at cross-purposes with the running of a sedentary society that depended on irrigated agriculture, commerce, public works, and systems of taxation. Prominent Persian bureaucrats and their descendents undertook these duties and were able to accrue considerable power and wealth, but often at a high cost when we read of the fates of Rashid al-Din, Saᶜd al-Dawla and Juvayni.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".