Specifics of the Russian Consular Service in the Ottoman Empire in the Last Quarter of the 18th — Early 20th Centuries
Bibliographic record
Abstract
The specifics of the activities of Russian consular institutions in the Ottoman Empire were studied in historiography using the examples of particular consuls in certain historical periods. The article attempts to trace the general patterns throughout the entire period of the functioning of the Russian consulates in the Empire — from the era of their appearance until the First World War. The archival and published documents related to the activities of the consulates show that the specifics of their work in the Ottoman Empire were largely determined by the peculiarities of the Empire itself: a state-legal system different from the European one, Islam as the state religion while maintaining many confessions among the population, multi-ethnicity, and the unequal nature of relations with European countries. In adapting to these conditions, the Russian consular service initially relied heavily on the French experience, while at the same time taking advantage of the presence in the country of a significant Orthodox population. The shortage of its own qualified diplomats had to be filled by hiring foreigners. The number of consular offices and their workload have increased over time, and the qualifications of the staff have improved. Throughout the period, the consuls solved not only commercial, but also political problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".