The Extent of Child Trafficking and National Response in Ethiopia: A Quantitative Study
Bibliographic record
Abstract
This article analyzes the extent of child trafficking from a quantitative standpoint. The quantitative research approach was adopted in this study, supported by the application of cross-sectional exploratory and descriptive research design, which was used to address the extent of child trafficking in Ethiopia. A total of 636 household respondents were selected by systematic random sampling technique to fulfil the adopted quantitative survey. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 24.0 statistical software packages. Adapt Quantitative-Logistic Regression, Bivariate analysis, Multivariate analysis, and Cross-tabulation of extent and factors of child trafficking were thoroughly quantified. The extent of child trafficking from the total result in the study area is about 128 children had been trafficked from the total of 636 households and that means the extent of child trafficking is about 20.1 percent in East Este Woreda and Debre Tabor woreda in or 1 child from 5 children had been trafficked. It recommended, the research is needed to identify the extent of child victims and vulnerable of trafficking in the study area to combat the problem. On the other hand, for a clear understanding of the International Conventions and Treaties (such as fully translating Rights of the Child and all ratified conventions), the publication should be translated to Amharic and Oromifa version (local language) in the official Negarit Gazeta of Ethiopia, so that everyone could understand and seek to implement their right easily. Information gup is a disadvantage for the nation, the government should use different programs on television and radio to address the issue.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".