Dramatizing Atrocities: Plays by Wale Soyinka, Francis Imbuga, and George Seremba Recalling the Idi Amin Era
Bibliographic record
Abstract
Idi Amin, who ruled Uganda from 1971 to 1979, was not the only dictator noted for atrocities. In Africa in the second half of the twentieth century, men of the proverbial iron fist rose and fell, to be replaced by others: Mobutu Sese Seko in Zaire, Macias Nguema in Equatorial Guinea, Emperor Bedel Bokassa in the Central African Republic, Sergeant Samuel Doe in Liberia, General Siad Barre in Somalia, Juvenal Habyarimana in Rwanda, Hissene Habn, in Chad, General Sanni Abacha in Nigeria, and so on. From other parts of the world, a roll call of some of the most notorious regimes would take in Cambodia's Pol Pot (Soloth Sar), Chile's Augusto Pinochet, Romania's Nicolae Ceaucsescu, and Yugoslavia's Slobodan Milosevic. The atroc ities associated with these rulers include torture and mass murder. The reoccurrence of such atrocities makes them seem almost banal. In representing atrocities, how can plays convey the full impact and implications of what has happened without succumbing to sensationalism? How can playwrights provoke a deep response from an audience, emotional and intellectual, rather than repulsion?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".