The Ambiguity of Betrayal: Contesting Myths of Heroic Resistance in South Africa
Bibliographic record
Abstract
Hegemonic practices of memorialization rely on narratives of heroic, morally untainted resistance, which cast traitors as the aberrant “other.” This paper draws on Simone de Beauvoir’s The Ethics of Ambiguity and historical and sociological accounts of betrayal to trouble this binary and construct a framework for memorializing betrayal in its ambiguity—in relation to the everyday reality of tragic dilemmas that resisters face. I show how attentiveness to the ambiguity of betrayal can help rethink heroic resistance myths beyond the exclusionary logic pitting moral purity against the depravity of treason—and warn against the reproduction of systematic practices of othering in the new political order. The paper develops the political relevance of this theoretical exploration via the example of a South African novel, The Texture of Shadows , examining how its insights into the ambiguity of betrayal challenge the myths of heroic resistance in South Africa.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.072 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".