Bibliographic record
Abstract
This article reads at how Nnedi Okorafor's novel Who Fears Death (2010) rewrites the traditional narrative, cultural events progressing with time as development, offering a vision of current and prospective African identity that is not simply based on colonial history, after an apocalypse, Africa suffered with.Okorafor's imaginative use of intertextuality and subversion of "sovereign narratives" works to construct an alternative model of identity, according to the study.The novel is set in a postapocalyptic Africa where one tribe, the Nuru, enslaves and oppresses another, the Okeke.Onyesonwu, a strong and determined heroine, sets out to rewrite the "Great Book" to strip her father, the despotic sorcerer Diab, of his powers.The novel tackles dictatorship through dictation, the influence of epistemic theories on culture and belonging, and tackling the problem of despotism and resistance in the content.The rewriting of the Great Book, which embodies the gripping storyline of oppression, makes room for a new narrative to emerge.Okarafor undermines linear notions of development within the story's structure by locating herself intertextually in a piece of research that crosses time, geography, and genre barriers along with the injustice and inequality females face.As a result, identity arises from a dynamic structure rather than from the accumulation of history.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".