‘The local<i>mwananchi</i>has lost trust’: design, transition and legitimacy in Kenyan election management
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".