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Record W2974846285 · doi:10.3968/11212

Flight, Anorexia and Madness Coexistence of Body and Spirit

2019· article· en· W2974846285 on OpenAlexvenueaboutno aff
Chunming Li

Bibliographic record

VenueHigher education of social science · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsAnorexiaState (computer science)SociologyAbnormalityAestheticsPsychologyPsychoanalysisGender studiesSocial psychologyLawPolitical sciencePhilosophyMedicineComputer science

Abstract

fetched live from OpenAlex

The Canadian writer Margaret Atwood has created various impressive women characters in her fictions. Facing the awkward situation, their bodies usually react first: flight, anorexia and madness, which present not only their abnormal state of body or physics, but also express their unvoiced desire, thoughts and spirit. Through the abnormality or even morbidity of the body, these females show great intensity of desires to rebel against phallocentrism, remove the fetter of men, acquire the right of freedom and liberty and achieve true equality with men in social, political, cultural and economical respects. These appeals fit amazingly the situation of Canadian literature, in which Canadian woman writers and man writers are equally successful and influential, and reflect the belief of Atwood that contemporary women are capable of compete with men in all the fields in the near future through their persistent struggling as well.

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: Other · Consensus signal: Other
Teacher disagreement score0.600
Threshold uncertainty score0.805

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.033
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.003
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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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