‘I Am Also Having Mother Once, and She Is Loving Me’: Reading Cary Joji Fukunaga’s Beasts of No Nation in a Post-Network Era
Bibliographic record
Abstract
Abstract This article examines Cary Joji Fukunaga’s film adaptation of Uzodinma Iweala’s Beasts of No Nation, with a special focus on the final scene, in which a counsellor is assigned to help the protagonist deal with the trauma of having been a child soldier. While the casting of a black African actor as the counsellor in Fukunaga’s film may appear to detract from the novel’s interrogation of the uneven power relations between Africa and America, an interpretation oscillating between novel and film reveals that there may be some benefits to erasing the white saviour figure from the scene. The erasure of a white American character not only redirects the focus to relations among Africans but also comments indirectly on the circulation of transnational films via streaming services such as Netflix. Reading in between adapted text and adaptation also yields some important insights about Beasts’ critical engagement with the politics of circulation, reception, and consumption of child-soldier narratives at a time when such narratives have become popular among transnational audiences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".