Magic Realism as Postcolonial Aesthetics in African and Afrodiasporic Literatures
Bibliographic record
Abstract
From their first novels, Ngũgĩ wa Thiong’o, Boubacar Boris Diop, and Toni Morrison have garnered critical attention as realist writers. If myth and legend were part of their literary productions, these were more often than not embellishments in the form of tales embedded within the main story. Yet Ngũgĩ’s sixth novel Matigari (1986), Morrison’s fifth novel Beloved (1987), and Diop’s sixth novel Doomi Golo (2003; translated into English as Doomi Golo, The Hidden Notebooks, 2017), are considered political fables that put myth and magic at the forefront. This observance raises questions as to why these engaging realist novelists shift to a magic realist approach. Is it appropriate to describe and to combat the modernity and complexity of the postcolonial system with an essentially anachronistic trope? What does magic realism allow them to express that cannot be achieved through bare realism?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".