Eliminating Rivals, Managing Rivalries: A Comparison of Robert Mugabe and Kenneth Kaunda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article seeks to explore the role that leadership plays in both the perpetration and avoidance of mass atrocities. Many scholars have argued that leadership is pivotal to the outbreak of such violence but there is almost no scholarship which explores the role that political leaders play in mitigating or aggravating the risk of atrocities over time. Why is it that mass atrocities occur in some places but not in others, despite the existence of similar risk factors? By conducting a comparative analysis of Robert Mugabe of Zimbabwe and Kenneth Kaunda of Zambia, this paper investigates the impact that the strategies of each leader had on the risk of mass atrocities. Both countries share similar colonial backgrounds, and display comparable structural risk factors commonly associated with genocide and other mass atrocities. Both Kaunda and Mugabe were key leaders in their countries’ liberation struggles, and both leaders played pivotal roles during the crucial formative years of independence. Yet the two countries have taken dramatically different paths – while Zambia has remained relatively stable and peaceful, Zimbabwe has experience mass violence and repression.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it