Disabled Woman/Nation: Re-narrating the Erasure of (Neo)colonial Violence in Ondjaki's <i>Good Morning Comrades</i> and Tsitsi Dangarembga's <i>Nervous Conditions</i>
Bibliographic record
Abstract
The article interrogates the erasure of violence through the use of disabled women's bodies as tropes in postcolonial African literature; it argues that the use of disabled women's bodies as symbols of the 'disabled' postcolonial nation creates a catharsis through which knowledge of the violence of (neo)colonial relations—the impact of which has been experienced as war and exploitation—is erased or suppressed. Through an application of Ato Quayson's typologies of disability representations to two contemporary African novels, the article contributes to a 'disabling' of both Postcolonial Literary Studies and to feminist anti-racist possibilities for Disability Studies by showing that disability representations in these texts serve to erase neocolonial violence. The article argues that the centrality of political catharsis in Ondjaki's Good Morning Comrades and Tsitsi Dangarembga's Nervous Conditions presents us with a different basis for aesthetic short-circuiting than does Quayson's conceptualization of a generalized fear of contingency and death brought on by an encounter with disability. Quayson's work gives many ideas about how this short-circuiting happens, but not why it happens. The article concludes that the answer can be found in the specific histories that are being suppressed and in the political choices that arise out of these histories.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".