Women Trafficking, a Humanitarian Cancer in Edo State: A Profiling Survey of Factors from Non-Governmental Perspective
Bibliographic record
Abstract
The Palermo Protocol established human trafficking (including women trafficking) as a global humanitarian crisis, as well, proposed the scope of intervention to include collaborative non-governmental networks. In Nigeria context, activities of Non-Governmental Organisations (NGOs) in one of the Nigerian hotbed states of women trafficking, Edo State, are more pronounced especially in the area of reintegration and rehabilitation. Despite these interventions, activities of women traffickers have not been significantly curtailed, in view of this, it was assumed that relevant NGOs in the state might be treating symptoms instead of causes of the scourge. An exploratory descriptive study was conducted to re-profile factors enhancing the hydra-headedness of the menace in the state. Data were collected from 129 field operators of relevant anti-women trafficking NGOs selected from the capital city of the state, Benin City. Factors identified as drivers of women trafficking in the state include but not limited to poverty, weak institutions, easy access to internet, globalisation, and greediness of victim’s family. In the end, it suffices that finding lasting solution is more to addressing the women exploitation in state, it goes beyond reintegrating and rehabilitating victim of women trafficking. Government at all level should redesign their approach to favour social and economic policies as the key instruments of state intervention against women trafficking.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".