Bibliographic record
Abstract
The history of Mesopotamian lexicography in cuneiform extends from the very beginning of writing (around 3200 BC) to the demise of cuneiform in the first centuries of our own era. Lexical texts accompany cuneiform writing in all periods and all areas where this writing system was used. They have been found not only in Babylonia (southern Iraq) and Assyria (northern Iraq), but also in present-day Iran, Syria, Turkey, Israel, and Egypt. One may think of lexical lists as something between a dictionary and an encyclopedia – they organize, transmit, and preserve knowledge. Lexical lists have drawn the attention of cuneiform scholars from the earliest days of Assyriology, because they explain how to read and understand the ancient writing system. As such, they were invaluable in the early days of decipherment, and they still fulfil that essential function in modern philological research. Lexical lists are the earliest scholarly genre in ancient Mesopotamia, and thus they may claim to be the earliest scholarly genre in the history of humanity. Lexicography, therefore, also plays an important role in discussions of the history of Mesopotamian scholarship and education.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".