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Record W4392524388 · doi:10.4467/k7501.45/22.23.18050

Baltic Germans in the Russian Imperial Navy: Navigators, Explorers, and Contributors to Place Naming

2023· book-chapter· en· W4392524388 on OpenAlex
W. Ährens, Sheila Embleton

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

VenueJagiellonian University Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsYork University
Fundersnot available
KeywordsNavyHistoryAncient historyGeographyArchaeology

Abstract

fetched live from OpenAlex

From the 13th century onwards, Germans spread northeastwards along the Baltic coast, the area now occupied by Lithuania, Latvia, Estonia, the St. Petersburg region of Russia, and Finland. Most of these Germans were active as merchants. While for most of this period Lithuania had Poland as an overlord and Finland had Sweden, in Estonia, Livonia, and Courland (now Estonia and Latvia) the Germans soon formed the ruling class. Not only were they merchants, landowners and military leaders, but they also basically formed the government of these regions. In 1710, Russia became the new overlord of these regions. As a result, the Germans in this area were obliged to serve in the Russian Imperial forces. The Germans rapidly gained leading positions in these forces. In the Russian Imperial Navy, Baltic German captains sailed in the North Pacific area, particularly along the coasts of Siberia and Alaska. We will look at some of these captains and their role in naming places they visited and having places named after them. Among the most prominent are Adam Johann von Krusenstern, Ferdinand von Wrangel, Fabian von Bellingshausen and Otto von Kotzebue.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.196
Teacher spread0.171 · 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