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Record W7039039267

Langue russe et mémoire soviétique dans "Tapka" de David Bezmozgis

2012· article· fr· W7039039267 on OpenAlexaboutno aff

Bibliographic record

VenuePériodiques Scientifiques en Édition Électronique · 2012
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInnocenceAllegoryCousinRussian literatureSoviet union
DOInot available

Abstract

fetched live from OpenAlex

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".

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.007
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

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