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Record W2584541419 · doi:10.5937/naslkg1634083n

Margaret Atwood's Life before man: Lesje Green's mental journey

2016· article· en· W2584541419 on OpenAlexaboutno aff
Milena Nikolić

Bibliographic record

VenueNasledje Kragujevac · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsThe ImaginaryCharacter (mathematics)Fictional universeRelation (database)Identity (music)GestureLiteratureIntrusionPerspective (graphical)PsychoanalysisSociologyHistoryArtPhilosophyAestheticsNarrativeComputer sciencePsychologyVisual artsLinguistics

Abstract

fetched live from OpenAlex

In this paper the author observes relation between fiction and reality of Lesje Green, the main female protagonist of Margaret Atwood's novel Life Before Man (1979) in the light of Lubomir Doležel's theory of possible worlds that had first been presented in his earlier papers from the 70s and 80s and then unified in Heterocosmics (2008). The parallel existence between the fictional (imaginary) world of the heroine (prehistoric Lesjeland) and the actual world of the text (twentieth century Toronto) is being determined. Namely, the heroine has a tendency to take mental journey that implies her transposition from one world to the other (the gesture of recentering), whereby her transworld identity is being established. The paper will identify and examine a numerous intrusions of imaginary into the real world and vice versa, that is to say, it will determine the points at which the fictional and real entities are being interwoven. It will be shown that the intrusion of reality initiates the process of heroine's liberating from fantasies. This will be achieved by the use of comparative and analytical methods of multidisciplinary character.

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)0.001
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.012
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.232
Teacher spread0.214 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueNasledje KragujevacSame topicThemes in Literature AnalysisFrench-language works237,207