‘The local<i>mwananchi</i>has lost trust’: design, transition and legitimacy in Kenyan election management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Across African democracies, maintaining popular trust in electoral management bodies (EMBs) is vital to enhancing election integrity and, ultimately, regime legitimacy. However, scholars have largely sidestepped any systematic analysis of how citizens formulate their attitudes towards EMBs and how these attitudes vary over time. To address these gaps in the literature, we focus on Kenyan EMBs, which have experienced fluctuating popular support since the ruinous 2007 elections and subsequent institutional reforms. Using primary election reports and original survey and focus group data, we analyse the sources of Kenyans' trust in EMBs from 1992 onward and probe the 2013 election period deeply. Across time, we find that confidence in EMBs usually collapses after polarised elections, due to perceived problems with the EMB's autonomy and capacity. Following the 2013 elections, Kenyans were also more likely to lose confidence in the EMB if they were affiliated with losing presidential candidates or if they were critical of EMB performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it