Bibliographic record
Abstract
The library of Burhān al-Dīn and the Ḥaram al-sharīf documents have accompanied us in various forms and shapes for more than a decade.We thus owe thanks to the many colleagues with whom we have discussed them and who offered advice at various points.We want to specifically express our gratitude to Linda Northrup (Toronto), who so generously shared her knowledge of the documents' discovery in the 1970s; Bashir Barakat (Jerusalem), who knows how important he has been for this project; Arafat Amro (Islamic Museum, Jerusalem), who has supported us in various ways; Yusuf al-Uzbaki (al-Aqṣā Library), who helped us with queries on Jerusalem manuscripts; Angela Ballaschk (Berlin), who made sure that grants (and much more) ran smoothly; Mohammad Ghosheh (Jerusalem/ Amman), who engaged with our detailed queries; Suzanne Ruggi (Salisbury), who saved us from many mistakes; and Christian Müller (Paris), who helped with his intimate knowledge of the documents.
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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.344 | 0.222 |
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".