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Record W4313409899 · doi:10.26843/dp.v18itematico.3793

(ENTRE) LINHAS E GRADES: o espaço da prisão em Hag seed, de Margaret Atwood

2022· article· pt· W4313409899 on OpenAlexaff
Gil Derlan Silva Almeida, Sebastião Alves Teixeira Lopes

Bibliographic record

VenueDiálogos Pertinentes · 2022
Typearticle
Languagept
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Entendendo o espaço como um importante ponto de análise nas obras literárias, bem como todas as interpretações que advém da consideração deste elemento como mais que um ponto sólido e físico no bojo desses estudos, este artigo objetiva analisar o espaço simbólico da prisão na narrativa da escritora canadense Margaret Atwood, mais precisamente em Hag Seed (2018). A obra que é uma releitura do clássico canônico inglês A Tempestade (2014), de William Shakespeare, desenrola suas ações na figura de um ex-diretor de teatro que busca vingança, e que dentro do espaço prisional revela-nos mais sobre sua identidade e sobre que pontos o mantém envolto nessa mística da prisão, que o encarecera, por vezes não só fisicamente, mas psicologicamente. Como metodologia, foi-se usada pesquisa qualitativa de cunho bibliográfico, e como aporte teórico, nomes como Brandão (2013); Foucault (1987, 2003, 2013); Goffman (2015) e Hall (2006). Podemos perceber que o espaço que aprisiona os personagens, institui essa privação para além do corpo físico, permeando o simbólico e refletindo nos comportamentos e ações das personagens que compõem o enredo da narrativa analisada.

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)0.003
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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
Published2022
Admission routes1
Has abstractyes

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