Factors Influencing Domestic Human Trafficking in Africa: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Human trafficking is a human rights violation in every region of the world. The African continent is not spared. Every year, millions of people experience significant health and social consequences. International organizations and governments combating human trafficking are hindered by a lack of knowledge about what factors influence domestic (within-country) human trafficking. OBJECTIVE: This study aims to conduct a scoping review to collate and synthesize literature on factors influencing domestic trafficking in Africa. METHODS: We will follow Arksey and O'Malley's framework to answer the question about reported influences on domestic human trafficking and their relative weight. The search strategy will explore PubMed, CINAHL, Web of Science, and Scopus. A total of 2 independent researchers will select quantitative, qualitative, or mixed methods studies that examine relationships influencing domestic human trafficking. We will document our results by following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We will extract a list of all reported relationships between identified factors influencing domestic human trafficking in each study. Based on a discourse analysis approach, we will weigh the strengths of the relationships based on how frequently they are reported across the included studies. We will summarize the findings as fuzzy cognitive maps depicting the relationships reported in the literature. The maps represent the influences between concepts (nodes) linked by arrows (edges) going from each cause to its outcomes. These maps are helpful visual summaries of the factors associated with domestic human trafficking, allowing a comparison with maps to be created by stakeholder groups. RESULTS: This project received financial support in March 2023. We expect to start the project in March 2024. We recruited 2 research staff members to conduct the scoping review and expect to publish the results in March 2025. CONCLUSIONS: The review will provide a comprehensive understanding of factors influencing domestic human trafficking in Africa. The overlap of human trafficking with other forms of exploitation, the limited literature on domestic human trafficking, and the likely diversity of factors are challenges for the review. We propose strategies to address these challenges. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/56392.
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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 | 0.101 | 0.117 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.018 |
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".