Bibliographic record
Abstract
The article focuses on a Canadian writer Alice Munro (born 1931), winner of the 2013 Nobel Prize in Literature. The paper analyzes the reasons underlying the nomination of the writer, who published about twenty various collections of short stories in 1968-2012. Alice Munro is a three-time winner of the Governor General Award, which is very prestigious in Canada. In 2009 Munro won the Man Booker International Prize. The introductory part of the article centers on Alice Munro’s work, it highlights similarities and differences between Alice Munro’s style and Chekhov’s one. It underlines Alice Munro’s popularity in Russia, which is manifested through the publication of interesting works devoted to Munro and through the organization of research conferences devoted to the writer. The main part of the article focuses on the analysis of Munro’s story dedicated to a famous Russian woman, an outstanding mathematician Sophia Kovalevsky. To analyze Munro’s story, the author of the article uses historical and literary approach, culturological and biographical methods of research, the auteur theory and the modes of discourse analysis. The author of the article investigates the historical background of Munro’s story, Sophia Kovalevsky’s real biography and the subjective interpretation of her image and her life by Alice Munro, who highlights romantic feelings of her character and her search for happiness. Special attention is given to the literary originality of the story. Munro is a master of third-person narrative, which Franz Stanzel described as “figural narrative situation”, and D. Zatonsky labeled as “narrative through characters’ point of view”.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".