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Land of cemetery: funereal images in the poetry of Musa Idris Okpanachi

2018· article· en· W2888847262 on OpenAlexaff
Uchechukwu Peter Umezurike

Bibliographic record

VenueTydskrif vir letterkunde · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPoetryTrope (literature)PoliticsParadiseState (computer science)Power (physics)DemocracyLiteratureHistoryLawArtArt historyPolitical science

Abstract

fetched live from OpenAlex

This paper focuses on Musa Idris Okpanachi’s poetry: The Eaters of the Living (2007), From the Margins of Paradise (2012), and Music of the Dead (2016). Nigeria, even after the military had relinquished power over a decade ago, is still faced with the issues that provoked the trope of protest in much of the poetry published between the mid-eighties and late nineties. Okpanachi’s poetry revisits these issues, demonstrating that democracy has been no less horrifying than military despotism. Dark, haunting images of blood, corpses, and cemetery recur in all three collections, depicting the regularity of death in the nation. I argue that Okpanachi employs funereal imagery to comment on the state’s morbid relationship with its citizenry. The Nigerian state is represented as murderous, so death fulfills its political objective. I conclude that although Okpanachi articulates a cynical commentary on postcolonial Nigeria, he marshals his creative energies to illuminate the political moment of his time.

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)0.001
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.041
GPT teacher head0.235
Teacher spread0.194 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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