Power Sharing as a Tool for Resolving Electoral Violence: Revisiting the Zimbabwean Experience
Bibliographic record
Abstract
This paper uses Hartzell and Hoddie’s four dimensions of power-sharing to analyse the implementation of the 2008 power-sharing in Zimbabwe and its impact on electoral violence. It interrogates the resolving of the Zimbabwean electoral violence through the use of power-sharing deal between the opposition and the ruling party. The theoretical explanations why electoral violence would occur in a country ruled by an authoritarian leadership suggests that, autocratic governments use electoral violence as a way of influencing the electoral outcome. The other position suggests that the weaker political party is the one responsible for electoral violence as it enjoys the monopoly of being the victim. This study dispels the notion that power-sharing has any impact on resolving electoral violence permanently, we argue that the resolving election dispute through power-sharing does not resolve the differences between warring parties rather, it gives temporary peace. The findings of this study support the position of the electoral authoritarian theoretical perspective that autocratic government will use violence and all the means necessary to ensure that they return the power.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".