The Economic History of the Medieval Middle East: Strengths, Weaknesses, and the Challenges Ahead
Bibliographic record
Abstract
One may say that our field has had a respectable crop of scholars engaged in research and numerous important publications to its credit. Past investigations of the agricultural sector have included excellent coverage of taxation systems and tax rates, good coverage of cultivation methods and crops, not very thorough coverage of landholding patterns, and almost no studies of productivity rates. For the manufacturing sector we have very good coverage of manufacturing techniques and good coverage of labor organization and division of labor but little on the productivity rates of individual sectors such as textiles, on apprenticeship and wages for either skilled or unskilled labor, or on the relationship of wages to prices. We have important studies on both regional and long-distance trade and commerce, including on routes and trade-related institutions and on tools of trade such as credit and investment partnerships (qirād/commenda), and related studies regarding urbanization, exchange, and markets. The auxiliary fields of numismatics and archeology have yielded important studies on coinage and minting and on settlement patterns that are likely to improve our grasp of the economic history of the medieval Middle East. We also have at our disposal volumes of statistical data, collected from literary and documentary sources, on prices, wages, commodities, weights, measures, and coins. Several online projects scrutinizing data from primary sources, mainly papyri and Geniza documents, yield more figures, though mostly on the economic history of early Islamic societies. Among the lacunae are studies related to topics such as economic institutions, property rights, standards of living and inequality, GDP estimations, sector productivity, market integration, exogenous shocks, and economic growth.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".