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Record W2598072292 · doi:10.3968/9260

Scars on Both Body and Mind: Trauma and Rage in Ola Rotimi’s Hopes of the Living Dead

2017· article· en· W2598072292 on OpenAlexvenueno aff
Omeh Obasi Ngwoke, Ene Eric Igbifa

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsRage (emotion)ParanoiaNeglectPsychoanalysisCharacter (mathematics)AestheticsPsychologyArtSocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

A tendency towards apparently unprovoked rage characterizes the actions of many a leper-character in Ola Rotimi’s Hopes of the Living Dead . This study attempts, therefore, to utilize insights from clinical psychology or, more specifically, trauma studies to seek out the roots of these characters’ paranoia. Relying on insights from such trauma theorists as Sigmund Freud, Cathy Caruth, Esther Giller and Glen Most, among others, the study traces the root of the leper-characters’ reactions to both internal and external stimuli in the colony to which they have been consigned by the authorities to the repressed sense of neglect and discrimination brought upon by their sequestration. The implied contention of this study is that a different, more humane course of treatment for the leper-characters which seeks to integrate them into, and not separate them from, the society of which they see themselves rightly as a part would have averted the all-too-frequent temper tantrums that suffuse the atmosphere of the play.

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 score0.006
Threshold uncertainty score0.013

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 studies0.0060.018
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.383
Teacher spread0.360 · 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
Published2017
Admission routes1
Has abstractyes

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