Extralegal payments to state officials in Russia, 1750s–1830s: assessing the burden of corruption
Bibliographic record
Abstract
Abstract This article uses the records of expenditures from a set of estates that belonged to the Golitsyn family to assess the level of ‘routine corruption’ in Imperial Russia in the late eighteenth and early nineteenth centuries. The data from these books allow us to identify individual cases of unofficial facilitation payments made by the estates and by peasant communes to district‐level officials; to delimit key types of payment situations; and to calculate the sums expended for payments by a given estate in a given year. The resulting numbers are compared to the overall volume of obligations borne by the serfs to the state and to their landlords. Our conclusion is that while the facilitation payments were ubiquitous and accompanied any interaction with the state, the volume of these ‘routine’ payments (as opposed to other forms of extraction) was quite low and they did not put a significant burden on the peasants, while at the same time securing hefty extra incomes for top district officials. Rather, by the last decades of the eighteenth century Russian Imperial officials at the district level might have switched from a tribute‐like extortion from the population at large to acquiring vast sums by collecting unofficial payments in more targeted ways.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".