Bibliographic record
Abstract
Sex trafficking is a type of violence perpetrated on women that takes place in diverse settings and usually involves many different actors. It is an aspect of human trafficking that is becoming pandemic in society and possibly, the fastest growing human rights violation in the world today. It has generated a lot of concern worldwide and especially in Nigeria where it is very rampant. Sex trafficking in females resembles the ancient dehumanizing slave trade. In most cases, it involves involuntary servitude and is therefore commonly referred to as modern day slavery. Sex trafficking thrives and goes on with impunity because several countries do not have tough anti-trafficking legislation in place and even when there are legislation in place, such laws are often not enforced to check the menace of sex trafficking due mainly to very influential people involved in this disgraceful act. Unfortunately few trafficking cases are prosecuted, and only a few actually result in convictions. What is more, fear and shame keep many women and girls from seeking help. Nigeria is one country that is deeply affected by sex trafficking and so has taken the bull by the horn by enacting a national law on human trafficking due to the high prevalence of sex trafficking in the country. This work examined the menace of sex trafficking particularly as it affects Nigeria and examined major legal framework in place to curb sex trafficking whilst ascertaining their adequacy or otherwise, and how the menace has been curtailed so far and proffered a solution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".