Intertextual Parallels between Gogol’ and Hoffmann: A Case Study of Vii and The Devil’s Elixirs
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".