Rhetorical Caricature: An Educational Reading of Nabokov's Treatment of Freud
Bibliographic record
Abstract
Vladimir Nabokov (1899-1977), Russian-American novelist and lepidopterist, was neither a didactician nor a moralist. His images, which he painted with his words, are deceptive and contradictory. But the strangest thing happens. It turns out that Nabokov disliked Freud and painted images of the psychoanalyst that seem to become stale and mechanical. Literary critics, philosophers, and others have had a tendency to criticize Nabokov for suffering from influence-anxiety when it comes to Freud, hence making Nabokov into a dull paternalist with strong and prejudiced convictions. My suggestion is that we go beyond the ‘anxiety of influence’ viewpoint and address the issue through a phenomenological study and base our judgments on the experiences of the phenomena that shine through different texts of Nabokov. Thus it will be possible to see, I argue, that Nabokov’s rhetorical caricature may evoke experiences that are educative. At the same time, Nabokov’s treatment of Freud can be cast in a new light.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".