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Record W2936786856 · doi:10.1017/s0010417519000082

Fugitives, Vagrants, and Found Dead Bodies: Identifying the Individual in Tsarist Russia

2019· article· en· W2936786856 on OpenAlex
Alison K. Smith

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComparative Studies in Society and History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSoviet and Russian History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmpireIdentity (music)Context (archaeology)Subject (documents)State (computer science)Identification (biology)HistoryGenealogyLawEstateIndependence (probability theory)Political scienceAestheticsArtComputer science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.220
GPT teacher head0.389
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it