Bibliographic record
Abstract
Violence against women and girls is one of the most prevalent human rights violations in the world.It knows no social, economic or national boundaries.Gender-based violence undermines the health, dignity, security and autonomy of its victims, yet it remains shrouded in a culture of silence.Victims of violence can suffer sexual and reproductive health consequences, including forced and unwanted pregnancies, unsafe abortions, traumatic fistula, sexually transmitted infections including HIV, and even death.This paper highlights the trends and implications of violence against women in the context of growing insecurity in Nigeria.This is because the achievement of Sustainable Development Goals (SDGs) are linked to the extent women are protected from violence.The paper argues for the abolition of harmful practices such as sexual violence and female Genital Mutilation (FGM), honour killing, early marriage, women trafficking and the kidnapping of women by the perpetuator of these acts as panacea for gender harmony in Nigeria.The paper recommends that the Nigerian government through state and non-state actions should put substantive measures in place to protect women from violence, violators of extant laws punished and that the several other bills on violence against women should be passed into law and enforced.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.958 | 0.936 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".