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Record W2935920168

The Lives of Others: Trauma and Precariousness in Sefi Atta's Everything Good Will Come

2018· article· en· W2935920168 on OpenAlexvenueno aff
Kayode Omoniyi Ogunfolabi

Bibliographic record

VenuePostcolonial text · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPleaNarrativeAttaArgument (complex analysis)Power (physics)Vulnerability (computing)Order (exchange)SociologyDualismSpace (punctuation)AestheticsHistoryGender studiesLawEpistemologyPolitical scienceLiteraturePhilosophyComputer securityLinguisticsArtBusiness
DOInot available

Abstract

fetched live from OpenAlex

One of the major concerns of Sefi Atta’s Everything Good Will Come is the vulnerability of women in a social order structured largely by patriarchal power that often excludes women as “other.” This otherness, in symbolic and material sense, sometimes produces pain of traumatic proportion. The paper explores the ways in which women’s suffering tends to constitute a voice that calls for help and how others respond to this plea. It deploys Judith Butler’s Precarious Lives: The Powers of Mourning and Violence as an analytical model because of its argument that one’s survival after a destructive event depends on acknowledging the suffering of others. Atta’s novel becomes an important template on which the idea of precariousness produces its signification. In other words, Everything Good Will Come shows in order to mitigate the pain of others it is important to enter into the space of mutual precariousness. More importantly, the paper suggests that this novel has done more than being sensitive to the traumatic pain of other people by enacting a narrative possibility for Nigerian women’s fiction that projects a different politics that privileges the sacrificial commitment to the cause of others.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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