An analysis of the annalistic sources of the early Mamluk Circassian period /
Bibliographic record
Abstract
The Mamluk Sultanate that dominated Egypt and Syria over slightly more than two centuries and a half (647-922/1250-1517), witnessed the development of a prodigious historiographical production. While the historiography of the Turkish Mamluk period (647-792/1250-1382) has been the object of thorough analyses to determine the patterns of interrelations amongst its authors and the respective value of its most important sources, that of the Early Circassian Mamluk period (roughly, the last quarter of the fourteenth/eighth and the first years of the fifteenth/ninth centuries) has not as of yet received proper attention. In this dissertation, this historiographical production has been surveyed and subjected to an analysis, the methodology of which was pioneered by Donald P. Little, one that consists of close word-by-word comparison of individual accounts in the works of Syrian and Egyptian authors who wrote about this period. The focus here was on specifically non-biographical historical material contained in mostly annalistic works. Amongst the results obtained during this research was the ultimate reliance, at different degrees and depths, of all historians on the works of five authors, namely Ibn Duqmaq (d. 809/1407), Ibn al-Furat (d. 807/1405), Ibn Hijji (d. 816/1413), al-Maqrizi (d. 845/1441) and al-'Ayni (d. 855/1451), but especially the first three.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".