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The theme of death in prose by Margaret Atwood

2025· article· en· W4408154307 on OpenAlexaboutno aff
Roksana Romanovna Naydenova

Bibliographic record

VenueLitera · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)LiteratureLinguisticsArtHistoryPhilosophyComputer science

Abstract

fetched live from OpenAlex

The subject of the research is the death in prose by the famous modern Canadian writer Margaret Atwood (b. 1939). The object of the research are the novels and short stories of the author (novels "The Blind Assassin", "Cat's Eye", "Lady Oracle" etc.). The author of the article pays special attention to the biographical narratives of the Canadian writer, in which the main character is also the narrator. Stories of storytellers by M. Atwood, as a rule, always unfolds retrospectively – from the present to the past. During their journey, the narrating characters mentally return to the past and conduct "negotiations with the dead." Based on the researches of M. Atwood's legacy, as well as on the literary works of the Canadian writer herself, the author of the article describes the place of the theme of death in her retrospective narratives. The author of the article comes to the following conclusions. 1) The theme of death is one of the key themes in her work. Atwood, since the retrospective narrative of the biography itself involves an appeal to the past, to the world of the dead. Remembering, the main character, the narrator, mentally makes a journey into the world of the past, which M. Atwood rhymes with the afterlife. 2) The creative process, the creation of a story in the writer's artistic world, takes place during "negotiations with the dead", when the hero-narrator mentally addresses people from his past. 3) Death in the works of M. Atwood often appears in the form of a relic, an archaeological find, a lost and rediscovered thing. The difficult process of exhumation and extraction from the ground is consonant with the equally difficult process of dissecting the narrator's own complexes and resentments.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.009
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.232
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

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