Authoritarian leaders and multiparty elections in Africa: How foreign donors help to keep Kenya's Daniel arap Moi in power
Bibliographic record
Abstract
This article argues that prior accounts of Moi and KANU's re-election in Kenya's 1992 and 1997 polls overemphasise divisions within the opposition and underestimate the role of international actors. Drawing on interviews with central players and internal donor documents, the author demonstrates that aid donors played a central part not only in initially advancing the cause of multipartyism but subsequently also, on several occasions, actively impeding further democratisation. Donors twice knowingly endorsed unfair elections (including suppressing evidence of their illegitimacy) and repeatedly undermined domestic efforts to secure far-reaching political reforms, which were a prerequisite for an opposition victory and a full transition to democracy. In the face of anti-regime popular mobilisation, donors' primary concern appeared to be the avoidance of any path that could lead to a breakdown of the political and economic order, even if this meant legitimising and prolonging the regime's authoritarian rule.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".