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Record W4379984960 · doi:10.3390/women3020024

Choice of Non-Disclosure as Agency: A Systematic Review of Non-Disclosure of Sexual Violence in Girlhood in Africa

2023· review· en· W4379984960 on OpenAlexafffund
Doris M. Kakuru

Bibliographic record

VenueWomen · 2023
Typereview
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsHarmAgency (philosophy)Sexual violenceDomestic violenceCriminologySexual abusePunishment (psychology)PsychologyPoison controlSuicide preventionMedicineSocial psychologySociologyMedical emergencySocial science

Abstract

fetched live from OpenAlex

Africa is home to 308 million girls below the age of 18 of whom at least 50% have experienced sexual violence, despite the existence of international treaties as well as pan-African and national policies aimed at eliminating violence. Past studies on sexual violence against girls have focused on the consequences of violence and the experiences of survivors, including the fact that most violence is not disclosed. Some studies that attempted to outline barriers to the non-disclosure of sexual violence do not acknowledge the agency of survivors, thereby indirectly portraying them as passive victims of these barriers who need protection by adults. The available studies have not analyzed ways in which the survivors’ choice not to disclose can be understood as a form of agency. This systematic review was conducted, therefore, to examine the causes of non-disclosure of violence from the survivors’ point of view. Findings show that often when girls choose not to disclose sexual violence, they are strategically protecting themselves from further abuse and harm, such as physical punishment for talking about sex, forced marriage, threats of death, etc. The findings of this review have implications for research, policy, and programming. For example, more child-focused methods should be used to further study the non-disclosure of sexual violence.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.374
Teacher spread0.308 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes2
Has abstractyes

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