Fugitives, Vagrants, and Found Dead Bodies: Identifying the Individual in Tsarist Russia
Bibliographic record
Abstract
Abstract In the middle of the nineteenth century, in the Russian Empire, a new set of state-sponsored provincial newspapers began to include notices seeking fugitives and trying to identify arrested vagrants and found dead bodies. The notices were part of a larger effort to match individuals with specific legal identities based in social estate ( soslovie ). In principle, every individual subject of the Russian Empire belonged to a specific owner (in the case of serfs) or to a specific soslovie society (in the case of nearly everyone else). The notices were an effort to link people who had left their proper place to their “real” identity. To accomplish this, the notices also made use of a kind of simple biometrics or anthropometrics in order to move beyond an individual's telling of his or her own identity. By listing height, hair and eye color, the shape of nose, mouth, and chin, and other identifying features, the notices were intended to allow for more exact identification. This version of identification developed out of previous practices grounded in the documentary requirements of the tsarist state, and they were slightly ahead of their time in the context of nineteenth-century developments in the sphere of identification practices. They were also distinct from other kinds of anthropometric practices of classification developed at the same time or soon thereafter—where many sought to use physical measurements to classify people by race or by inclination to criminality, the Russian system had no such goals.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".