Building a Political Settlement: The International Approach to Kenya’s 2008 Post-Election Crisis
Bibliographic record
Abstract
This paper presents findings from an empirical study of the 2008 Kenyan crisis, aimed at exploring the role and effectiveness of the international development and diplomatic communities’ response. This response involved working to ameliorate the fragile political environment that followed the disputed 2007 elections. Thus, this case study was selected as an archetype to demonstrate how international actors can work cooperatively on political settlements. The key objective of this research is to analyse and share lessons about how those international actors present in Kenya engaged with the evolving political settlement to address the conflict. This study draws upon evolving political economy and political settlement debates in its analysis and uses the Organisation for Economic Co-operation and Development’s (OECD) Fragile States Principles as a framework. Adopting such an analytical lens encourages examination of the multiple, context-specific underlying dynamics that influenced the role of international actors during this period of political transition. It also enables a study of the operational factors facing external actors when they attempt to work more politically, and recognition of how carefully these actors need to use the limited role they have in shaping the internal institutional arrangements and dynamics of the countries within which they work. The study’s main findings indicate that in the 2008 post-election period the international development and diplomatic communities collectively commanded substantive influence over the nature and trajectory of Kenya’s evolving political settlement. It argues that these actors enhanced their influence over many important political issues principally as a result of applying good practice in fragile situations: understanding the context, adopting a unified and legitimate stance, coordinating and collaborating closely and acting fast to prevent conflict. They also laid the foundations for statebuilding in order to address the causes of the conflict. Prior to the crisis, a cohesive political voice was missing and many international actors lacked a thorough understanding of Kenya’s underlying political dynamics. This study concludes that the international development and diplomatic communities—by coming together and following good practice—significantly increased their influence over the political settlement following the 2008 crisis in Kenya.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".