Democracy unravelled in Kenya: multi-party competition and ethnic targeting
Bibliographic record
Abstract
The introduction of competitive multi-party politics in Kenya has led to intense struggles for the ultimate political prize, the ‘imperial presidency’. Given the country's multi-ethnic character which is dominated by five large ethnic communities or tribes, parties tend to be erected on ethnic foundations. Strong political personalities are elevated to represent and advance the interests of their people. Given the power and resources associated with the capture of power, electoral competition becomes a struggle for ethnic dominance. The Kikuyu who were the first community to rise to power after independence see the presidency as belonging to them. The others however seek to marginalize and/or displace the Kikuyu at every opportunity. In 2007, two political titans – Mwai Kibaki of the Kikuyu and Raila Odinga of the Luo – fought a harsh and virulent campaign which ended in a deeply flawed vote count. Kibaki won but Odinga claimed a stolen election. Immediately, severe ethnic violence was wreaked on one community only to be followed by revenge violence on others. The country came perilously close to collapse. The pattern of political violence to wound and destroy ethnic opponents arose in 1992, then in 1997 and finally in 2007. Multi-party electoral competition has brought untold grief to hundreds of thousands of Kenyans. In essence, democracy has become a curse for ordinary Kenyans.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".