Multi-ethnic vision or ethnic nationalism? The contested legacies of Anderson Mazoka and Zambia’s 2006 election
Bibliographic record
Abstract
Studies on elections in Africa’s multiparty democracies stress the role of “incumbency advantages” in the re-election of presidents. While this blanket explanation holds true for the most part, it does not cover the successes of incumbents in polls conducted with minimal levels of clientelism and manipulation. Using the example of Zambia’s 2006 election, this paper shows how incumbents in multi-ethnic societies attempt to build a winning coalition through effective appeals to ethnic inclusion. After the main opposition leader Anderson Mazoka died four months before the election, President Levy Mwanawasa appropriated Mazoka’s legacy as a politician committed to ethnic inclusion. He successfully presented himself in non-ethnic terms, accused his rivals of being tribalists and urged voters to reject them. Mwanawasa won over key sections of the opposition’s base and an election he was widely expected to lose, demonstrating the value of studying the role of individual political leadership in incumbent-party hegemony.
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How this classification was reachedexpand
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".