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Record W3201690741 · doi:10.3968/12218

Metaphoric Diagnosis and Aesculapian Comorbidity of Nigeria in Iwu Jeff’s Verdict of The Gods

2021· article· en· W3201690741 on OpenAlexvenueno aff
Iwabi Abraham Modahunsi

Bibliographic record

VenueStudies in literature and language · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsVerdictIgboLiteratureRemorseLawHistorySociologyPsychoanalysisPhilosophyMedicinePsychologyPolitical scienceArtPsychiatry

Abstract

fetched live from OpenAlex

There have been brief reviews of Jeff’s Verdict of the Gods online, since its publication in 2020, but there is a vast dearth of critical exegesis of the text. Among the four blurbs on the back page of the dramatic text, only one makes cursory remark about it as a reflection of the Nigerian problems. Beyond such casual reference, this study foregrounds metaphoric diagnosis and aesculapian comorbidity of the plagues threatening the wellbeing of Nigeria and her unity in the text. This gives the play dual interpretative mode – literal and metaphoric – just like Albert Camus’ The Plague and Tony Marinho’s The Epidemic . Like the precursory novels, Verdict of the Gods presents the scenario of a community gone awkward, one in which the gods of healing are irked, when the community is plagued by disease and death, because of the blood of innocent citizens shed by the venal and rapacious leaders. The use of Igbo setting, proverbial witticism and lexes provide a profound proof that the play caricatures, in a satiric form, the contemporary Nigerian sociopolitical scene, using the Greco-Roman medical practice. Jeff cautions Nigeria, that the blood of innocent people killed, or those who die, as a result of the actions or inactions of the leaders is the reason the society may not “see progress” (p.73). The solution to the problems is patriotism and atonement of sins by the rulers and their accomplices.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.238

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 studies0.0000.000
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 head0.029
GPT teacher head0.283
Teacher spread0.253 · 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
Published2021
Admission routes1
Has abstractyes

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