The Theurgy of Impurity: <i>Fin‐de‐Race</i> and Feminine Sin in Russian and Ukrainian Modernisms
Bibliographic record
Abstract
Much of women's writing emerged as a response to misogynic interpretations of the feminine in androcentric tradition. This paper examines intertextual affinities between the Russian modernist poet Anna Akhmatova (1889–1966) and Ukrainian modernist poet Natalia Livyts'ka‐Kholodna (1902–2005) in the context of the European negative symbolism of the feminine; and as a manifestation of transnational modernist practices. It demonstrates how the representation of female sinfulness in misogynic mythology, in which women appeared as demonic‐vampiric, heterodox seductresses, involved a mapping of purported feminine moral deficiency onto the discourse of Orientalism. Identifying how women's writing in Ukrainian and Russian modernisms mimicked androcentric cultural symbolism, the paper demonstrates the complexity of this mimetic function, which simultaneously preserves the symbolic tradition of its origin while in effect canceling it. Specifically, it investigates the poetics of sin as a common thread between Livyts'ka‐Kholodna and Akhmatova. Focusing on the concept of sin enables us to vividly reconstruct the logic of cultural conventions that cast the woman's role as negative, and to trace how female authors departed from these conventions. The study analyzes, in particular, three aspects of the thematics of sin and sinfulness: temptation, heterodoxy, and betrayal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".