The Appointment of Commissioners of the Independent Electoral Commission in Botswana
Bibliographic record
Abstract
Abstract Traditionally, Botswana has enjoyed the illustrious reputation of being a beacon of democracy in Africa and beyond. This reputation has been attributed to the country’s political and economic stability, free and fair elections, and peaceful transitions of government. Until recently, Botswana’s electoral laws and processes were rarely ever the subject of judicial scrutiny. However, in the aftermath of the 2019 general elections, there was a spate of election petitions seeking to impugn the outcome of the elections. Furthermore, in the build up to the 2024 general elections, litigation challenging electoral laws and processes became commonplace. These cases ranged from demands by parties to be actively involved in observing voter registration for elections to objections to the appointment of Commissioners of the Independent Electoral Commission; and demands for electronic copies of the voters roll. Collectively, the cases highlight potential inadequacies in Botswana’s electoral laws. This paper analyses the case of Umbrella for Democratic Change and Others v Attorney General and Others (UDC v AG). Therein, 11 opposition parties sought to interdict Commissioners of the Independent Electoral Commission from discharging their duties pending a determination of the constitutionality of their appointment. The paper poses and answers the simple question whether the court got the decision wrong.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".