The 30% Gender Quota Law in Sierra Leone: A Game Changer for Women’s Access to Parliament?
Bibliographic record
Abstract
Abstract The June 2023 elections in Sierra Leone occurred against the backdrop of amendments to the Public Elections Act (pea), the enactment of the Gender Equality and Women’s Empowerment Act (gewe), and a switch from the First Past the Post (fptp) electoral system to the Proportional Representation (pr) electoral system. For the first time in Sierra Leone’s history, law reforms, among other things, introduced a 30% gender quota rule for parliamentary and councillorship seats. After the 2023 elections, women won 41 out of the 135 elected parliamentary seats, increasing the percentage of women parliamentarians from 12.32% in the 2018 elections to 30.37%. Despite the increase, there have been concerns about the overall effectiveness of the gender quota rule in facilitating women’s access to parliamentary seats. The gender quota rule is set below 50%, is not accompanied by the candidates’ ranking order, does not apply in the election of 14 paramount chiefs, and operates within poor data desegregation of the candidates. This article highlights the required reforms to address the identified legal challenges to facilitate women’s equal access to representation in Sierra Leone’s Parliament.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".