The Ḥaram Collection and it’s importance for studying the history of Jerusalem during the Mamlūk’s Days
Bibliographic record
Abstract
In an article published in December 1978 by two young scholars, Amal A. Abul-Hajj (Palestinian) and Linda L. Northrup (American) there was announced an archeological discovery of great importance for the study of medieval Islamic history. On August 19, 1974 there had been found in the Islamic Museum in Jerusalem, a group of 354 complete documents and many other small fragments. The photographs of the documents are kept at the Museum and the McGill Institute of Islamic Studies. Up until today a number of articles and books on the Ḥaram documents have been published. The Ḥaram collection consists of 883 separately cataloged documents, majority of which come fourteenth (Christian) century and relates to transactions or records of events from Jerusalem under the Burji Mamlūks. The article exposes an overview of the collection with an emphasis on the various types of documents and the issue of the Ḥaram documents' significance for studies on Islamic diplomatic, Islamic law and the history of Jerusalem under the Mamlūks. The special focus is on the detailed analysis of one document, i.e., the Ḥaram 102 which together with presented comparison of the documents discussed by Huda Lutfi in her article 'A Study of the Fourteenth Century Iqrârs from al-Quds Relating to Muslim Women' gives a unique opportunity to acquire some knowledge about the common life of the medieval Muslim woman.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".