Secret Societies and Women’s Access to Justice in Sierra Leone: Bridging the Formal and Informal Divide
Bibliographic record
Abstract
In Sierra Leone, customary systems of governance have long been recognized as feasible alternatives to justice provision, particularly as formal institutions have yet to adequately address the barriers women face in accessing justice. However, the focus has often been on the chieftaincy, an institution largely dominated by men. In this paper, Women’s Secret Societies are explored under the premise that such institutions might be better at providing access to justice for women. The paper shows that customary institutions, including Women’s Secret Societies, by embracing cultural norms and values that undermine women, cannot necessarily serve as a better alternative for women to access justice. While these societies can be important sources of power, they are constrained in terms of what they do for women given their continued association with cultural practices like female genital mutilation and the shifting norms regarding the importance of their role in Sierra Leone society. Additionally, competition between formal and informal sources of power, particularly in the domain of sexual and gender based violence (SGBV), provides some limitations regarding what they can do in this arena. At the same time, given that culture is not static, the paper explores the possibility of engaging with these organizations in ways that could help complement the justice services available to women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".