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Record W2792907498 · doi:10.5334/sta.604

Secret Societies and Women’s Access to Justice in Sierra Leone: Bridging the Formal and Informal Divide

2018· article· en· W2792907498 on OpenAlexvenueno aff
Fredline M’Cormack-Hale

Bibliographic record

VenueStability International Journal of Security and Development · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsSierra leonePremiseEconomic JusticeSociologyInstitutionPower (physics)Political scienceGender studiesLawSocioeconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.327
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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