Bibliographic record
Abstract
Данная статья посвящена описанию и анализу работы комического в творчестве известной современной канадской писательницы М. Этвуд. Комическое в произведениях автора подается завуалированно. Избегая открыто пользоваться в своих книгах приемами по созданию комического эффекта, писательница играет и шутит с читателем при помощи сложной нарративной структуры. Юмор М. Этвуд основан на способе подачи материала, все ее произведения - это тотальное пространство ее героев-рассказчиков, чья цель заключается в том, чтобы привлечь слушателей и убедить их в своей правоте. Для этого они используют многие манипулятивные нарративные приемы при рассказывании истории. Например, создание и смена масок, псевдо-интрига, постулирование тезиса и его последующая карнавализация, разоблачение и др. This article is devoted to the description and analysis of the work of the comic in the creative art of the famous contemporary Canadian writer M. Atwood. The comic in the works of the author is presented in a veiled way. Avoiding open use of comic effect techniques in her books, the writer plays and jokes with the reader using a complex narrative structure. M. Atwood's humor is based on the way the material is presented, all her works are controlled by her storytelling characters, whose goal is to attract listeners and convince them that they are right. To do this, they use many manipulative narrative techniques when telling a story. These are, for example, the creation and change of masks, pseudo-intrigue, postulation of the thesis and its subsequent carnivalesque, exposure, etc.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".