Langue russe et mémoire soviétique dans "Tapka" de David Bezmozgis
Bibliographic record
Abstract
David Bezmozgis is one of the most famous representatives of a generation of Russian- Jewish- American writers born in the Soviet Union in the 1970s. "Tapka", one of his first short stories published in 2003, is set in the 1980s. It is the story of a Russian dog called "Tapka", loved and betrayed by the six-year-old narrator and his seven-year-old cousin in Toronto where the dog's owners, the Nahumovskys, and the narrator's family have recently emigrated. The Nahumovskys come from Minsk, and the narrator's family from the Baltic States. Left in the care of the narrator and his cousin, Tapka is ultimately run over by a car as the narrator attempts to shift the responsibility for the dog onto his cousin. On the surface, this story seems to be essentially an education, a passage from innocence to experience, and an allegory on Soviet (Jewish) memory represented by "Tapka". My paper shows that the story also focuses on the relationship between two languages, Russian and English, between the mother tongue and the second language which all the characters have to learn as immigrants. This relationship turns out to be more complex and interesting than a mere conflict between two languages, in which one language eventually destroys the other : it is also a cross-cultural interweaving in which the linguistic domination of English is subtly subverted by the Russian signifier epitomized by "Tapka".
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".