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Record W2186339966 · doi:10.82308/29236

The global dimensions of Britain and France's Crimean war naval campaigns against Russia, 1854-1856

2012· article· en· W2186339966 on OpenAlexfundno aff
Andrew C. Rath

Bibliographic record

VenueeScholarship@McGill (McGill) · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary, Cultural, Historical Analysis
Canadian institutionsnot available
FundersMcGill University
KeywordsContext (archaeology)PeninsulaEmpireDiplomacyChinaScholarshipAnnexationHistoryPolitical scienceAncient historyGeographyEthnologyArchaeologyLawPolitics

Abstract

fetched live from OpenAlex

The Crimean War was fought far outside its namesake peninsula in the Black Sea Region. Between 1854 and 1856, Anglo-French naval forces attacked the Russian Empire in the Baltic, White Sea, and Pacific. These campaigns receive little attention from modern historians, and much of the work that does exist relies on a limited number of English-language sources. This dissertation, on the other hand, is a comprehensive examination of these campaigns built on a foundation of primary documents written in English, French, and Russian. It also synthesizes relevant secondary scholarship in order to provide a comprehensive background for the three major European belligerents and to consider the perspectives of the other polities impacted by the conflict, specifically Sweden-Norway, Denmark, China, and Japan. This work's approach yields a more complete understanding of the worldwide context in which the Crimean War occurred. Ultimately, the wide-ranging imperial conflict that emerges starkly contrasts with customary depictions of the conflict as a petty, regionalized example noteworthy only as a cautionary tale of failed diplomacy and generalship or as a venue for advances is battlefield medicine, journalism, and photography.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0080.009
Scholarly communication0.0080.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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