Bibliographic record
Abstract
Interest in philology by the Arabs is reported in the sources to have begun only a few decades after the Qurʾān had been revealed to Prophet Muḥammad. The Hegira of Muḥammad from Mecca to Medina in AD 622 marks the beginning of the Islamic calendar, and thus the first century after the Hegira (AH) corresponds to the seventh century AD. But it is not until the second half of the second/eighth century that the earliest works in both grammar and lexicography were authored. Sībawayhi’s al-Kitāb laid the foundations of the grammatical tradition, and most later authors were faithful to its analytical methods, arguments, terminology, and scope. Sībawayhi was deeply influenced by his teacher al-Ḫalīl b. Aḥmad, himself the author of the first (non-thematic) Arabic lexicon, Kitāb al-ʿAyn . But although grammar and lexicography became two distinct disciplines at a very early stage, they shared much of their material and were both closely related to other linguistically oriented sciences, such as qirāʾāt (Qurʾānic readings), Ḥadī (Prophetic tradition), fiqh (jurisprudence), and tafsīr (exegesis).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.465 | 0.418 |
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".