Bibliographic record
Abstract
The article raised the question of dialogue national literatures. The urgency of this problem is caused by the need to study the processes and outcomes of interaction between different ethnic, cultural and philosophical systems, reflected the artistic consciousness of the modern era. This problem is discussed in the article on the material of the Nobel Prize for Literature (2013) contemporary Canadian writer Alice Munro (b. 1931). The purpose is to identify the conditions and methods of artistic interpretation of Russian literary classics in the works A. Munro, which is a favorite genre of story. By “Russian theme” here refers primarily to images, motifs and plot situations Russian classical literature, which act in the art world as factors A. Munro encoding ideas about Russia. The methodology of the study put the comparative historical approach, combined with elements of historical and cultural, biographical, intertextual and hermeneutical methods. It is proved that the basic way of introducing classical pretext to text stories A. Munro is an allusion. In this allyuziynost may be inherent in both story line of the Canadian writer of short stories and individual images or motives. Allyuziynosti source most frequently used Russian classic novel, first of all Leo Tolstoy novel. And this despite the fact that the North American literary criticism for the name stuck A. Munro “our Chekhov”.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".