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Record W6962197977 · doi:10.15826/usos.2023.13.4

Algeria of the 1850s in the Travel Prose of a Russian and French Traveler (V. A. Vonlyarlyarsky and E. Fromentin)

2023· article· en· W6962197977 on OpenAlexaboutno aff

Bibliographic record

VenueUral survey of oriental studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOrientalismRomanticismColonialismTravel writingStyle (visual arts)Front (military)Quarter (Canadian coin)Romance

Abstract

fetched live from OpenAlex

The article compares the travel prose of Eugene Fromentin and Vasily Alexandrovich Vonlyarlyarsky, created by them on the basis of impressions from travels in North Africa in the late 1840s – early 1850s. These writers belonged to the same generation, were at the beginning of their creative path and were influenced by romanticism that had already left the stage. Fromentin, later a well-known Orientalist painter, writer and art historian, traveled in search of fresh artistic experiences. Vonlyarlyarsky sought physical and mental healing in North Africa. The scene of the pseudo-documentary (but based on real events) travel prose of both authors was the city of Algiers in the first half of the 19th century, as well as the region adjacent to it. Their works very clearly and from different points of view reflected the turbulent era. The contradictions were primarily due to the colonial European expansion. The domestic tradition of oriental travel cannot be called developed at that time. The East was depicted by Vonlyarlyarsky without much reverence, while Fromentin wrote in line with the developed literary tradition of French literary travels. The essays of the Russian writer have a sharp anti-colonial orientation, which is not typical for French authors. At this stage of the study, the author limited himself to only the most general comparison, on the basis of which a conclusion is made about the fundamental similarity of the positions of both authors.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.880

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 studies0.0000.001
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 head0.087
GPT teacher head0.273
Teacher spread0.185 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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