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Record W1978161503 · doi:10.1163/221023912x642709

Intertextual Parallels between Gogol’ and Hoffmann: A Case Study of Vii and The Devil’s Elixirs

2013· article· en· W1978161503 on OpenAlexaff
Svitlana Krys

Bibliographic record

VenueCanadian-American Slavic Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsParallelsDepictionLiteraturePlot (graphics)Power (physics)Reading (process)ArtGermanPhilosophyUkrainianLinguistics

Abstract

fetched live from OpenAlex

Critics have noted similarities between Nikolai Gogol”s three early horror stories ( Vecher nakanune Ivana Kupala [St. John’s Eve], Strashnaia mest’ [A Terrible Vengeance], and Vii ) and the works of his famous German predecessor Ludwig Tieck. While some scholars have speculated on the relationship between his Ukrainian tales and the works of E.T.A. Hoffmann, detailed comparisons between the two authors have been limited only to Gogol”s “St. Petersburg” stories. By comparing and contrasting Gogol”s Vii to Hoffmann’s Die Elixiere Des Teufels [The Devil’s Elixirs], this article argues that the “Ukrainian tales” also betray some influence of Hoffmann, and that, in particular, there are intertextual connections between these two stories. This is evident on several levels: (1) in the similar depiction of the monstrous beings that appear to the protagonists in both works and influence their lives; (2) in the transformation of the protagonists into villains under the power of evil forces; (3) in the presence of a sinister double, at the “hands” of which the protagonists find their death; and (4) in the doubling of a female into an innocent and a corrupt (lustful) being. The paper contends that Gogol’ was recapitulating, consciously or unconsciously, Hoffmann’s The Devil’s Elixirs in Vii both in terms of plot detail and, as my psychoanalytical reading shows, also on the level of latent content found in both works.

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)0.000
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 score0.287
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
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 head0.035
GPT teacher head0.276
Teacher spread0.241 · 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.

Study designQualitative
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

Citations1
Published2013
Admission routes1
Has abstractyes

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