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Record W2783839869 · doi:10.3968/9921

Marxism and Sam Aihimegbe’s Blood in the Creek

2017· article· en· W2783839869 on OpenAlexvenueno aff
Oluwagbemiro Isaiah Adesina

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican studies and sociopolitical issues
Canadian institutionsnot available
Fundersnot available
KeywordsProletariatMarxist philosophyBourgeoisieIdeologyWorking classCriticismClass conflictPetite bourgeoisieSociologyNarrativeClass analysisCapitalismPoliticsPolitical economyNeoclassical economicsLawPolitical scienceLiteratureEconomics

Abstract

fetched live from OpenAlex

Marxist theory or Marxist criticism, one of the theories used in literary criticism, is based on the ideologies of Karl Marx and Friedrich Engels who argue that all societies (with the exception of primitive hunter/gatherers) are divided along class lines and are characterised by class struggle. This paper examines Sam Aihimegbe’s Blood in the Creek  as a reflection of Marx’s explanation regarding the class struggle between the bourgeoisie and proletariat resulting from economic, political and social imbalances. Marxism here is used as a lens to unveil how the capitalists: Government, their friends and oil companies explore the oil resources of Odi and other parts of the Niger Delta only for their financial benefits without consideration of the proletariat, the working class. In the face of uneven distribution of resources among the strata of the society, the masses revolt and this revolution is met with stiff resistance from the oil benefactors. This paper argues that studying Blood in the Creek  from a Marxist perspective assists to reveal layers of crisis between the capitalist and the working class. It uses literature to x-ray oil issues raised in the narrative, and seeks to proffer solution to the crisis between the bourgeoisie and proletariat.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.163
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.037
GPT teacher head0.412
Teacher spread0.375 · 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
Published2017
Admission routes1
Has abstractyes

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