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Record W4403082846 · doi:10.1080/18125441.2024.2363233

Voices From the Fringes: The Eco-Poetics of Niger Delta Women

2024· article· en· W4403082846 on OpenAlexafffund
Kufre Friesenhan

Bibliographic record

VenueScrutiny2 · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPoeticsNiger deltaDeltaArtGender studiesGeographySociologyLiteraturePoetryPhysicsAstronomy

Abstract

fetched live from OpenAlex

To say that the Niger Delta literature or the poetics of extraction in the Delta has attracted numerous scholarly engagements is an understatement. However, most of the critical works written about the Niger Delta have been by male writers such as Gabriel Okara, Ken Saro-Wiwa, Tanure Ojaide, Ogaga Ifowodo, Nnimmo Bassey and others who are known for drawing attention to the environmental despoilment and the suffering of the various Indigenous communities of the Delta. In this article, I am interested in the framing of the Delta by female poets from the region. Building on Gayatri Spivak’s concept of the subaltern, I ask, are Niger Delta women writing? And if so, how do they contextualise women and nature in their poetic imagination? Mindful of the complexities in the region, what literary devices do they employ to aid their navigation of the murky confluence of state power, multinational corporations, and environmental degradation that abounds in the region unabated? This work applies the theoretical positions of ecofeminist scholars to the close reading of select poems from Sophia Obi’s Tears in a Basket (2005), Ekaete George’s Saints and Scoundrels (2018), and Iquo DianaAbasi’s Coming Undone as Stitches Tighten (2021).

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.014
Scholarly communication0.0090.005
Open science0.0000.004
Research integrity0.0020.003
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 head0.024
GPT teacher head0.228
Teacher spread0.204 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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