Bibliographic record
Abstract
the third quarter of the nineteenth century worldeconomic conditions were exceptionally favorable for agricultural foods and raw materials produced and exported by the peripheral areas. The increased demand on the part of the new European industries, the relative decline in costs and prices of core products brought about by technological development, the eagerness of the British to purchase from the periphery in order to clear Great Britain's accounts, and finally, the Crimean and American Civil Wars were among the factors that encouraged a closer integration of peripheral areas into the global division of labor. Ottoman and especially western Anatolian agriculture responded well to this mid- Victorian pull. This can be attested to by the upward trend in the rate of growth of commodity production, and of intraregional and foreign trade, and by an increasingly rapid circulation of commodities, capital, and labor that took place in the Ottoman Empire during these years.1 The transmission of world-economic trends to western Anatolia is usually attributed to the successive intermediation of two groups: The first in line were foreign merchants who were in coastal towns and who had come as representatives of metropolitan firms or were in close contact with them. Greek and Armenian merchants and bankers were present both in the interior and on the coast, and they provided the linkage between foreign merchants and the sites of production in the hinterland. When analyzing the social position of this latter group, Ot* Research for this paper was made possible by a grant from the National Science Foundation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".