The missing link between legal age of sexual consent and age of marriage in sub-Saharan Africa: implications for sexual and reproductive health and rights
Bibliographic record
Abstract
Child marriage is a fundamental violation of human rights and a threat to access to education, sexual and reproductive health care, and employment. It also threatens freedom from violence, reproductive rights, movement, and the right to consensual marriage. In most countries in sub-Saharan Africa, the legal age of marriage is 18 years. Hence, girls who marry before 18 years are considered as victims of child marriage. Closely knitted to legal age for marriage is the issue of age for sexual consent, which refers to the minimum age at which a person is considered to have the legal capacity to consent to sexual intercourse. While there seem to be a standard legal age for marriage, the legal age for sexual consent varies in most countries in sub-Saharan Africa and is often lower than the legal age of marriage. In this commentary, we argue that the gap between the legal age of sexual consent and marriage partly accounts for some of the sexual and reproductive health challenges such as intimate partner violence, sexually transmitted infections, adolescent pregnancy, early childbirth, including unsafe abortions among adolescent girls in sub-Saharan Africa and infringements on their sexual and reproductive health rights. This commentary highlights strategic potential interventions that could help address the identified gaps. We argue that aligning the age for sexual consent and marriage is not the solution to the problem. However, what is critical is the education of young people about sexual and reproductive health issues and comprehensive sexuality education through advocacy networks at the national and local levels. Thus, the key is to provide accurate, timely, and non-judgmental sexual and reproductive health and rights information to young people irrespective of the prevailing age of consent. This provision will empower them to make informed decisions about their sexual and reproductive health.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".