Making sense of child, early and forced marriage among Syrian refugee girls: a mixed methods study in Lebanon
Bibliographic record
Abstract
INTRODUCTION: The Syrian conflict has resulted in over 2.3 million child refugees in the Middle East and the prevalence of early marriage has reportedly increased among displaced Syrian families. This study explores the underlying factors contributing to child marriage among Syrian refugees in Lebanon with the goal of informing community-based strategies to address the issue. METHODS: In July-August 2016, trained interviewers collected self-interpreted stories in Lebanon using Cognitive Edge's SenseMaker, a mixed-method data collection tool. Participants included married and unmarried Syrian girls, Syrian parents as well as married and unmarried men. Each participant shared a story about the experiences of Syrian girls and then interpreted the story by plotting their perspectives on a variety of questions. Patterns in the responses were analysed in SPSS and the accompanying qualitative narratives were reviewed to facilitate interpretation of the quantitative results. RESULTS: 1422 self-interpreted stories from 1346 unique participants were collected with 40% of shared stories focused on (n=332) or mentioning (n=245) child marriage. Quantitative data summarised the different perspectives of female and male participants. Syrian girls and mothers were more likely to share stories about protection/security and/or education and were more likely to report that girls were overprotected. Male participants were more likely to share stories about financial security as well as sexual exploitation of girls and more often reported that girls were not protected enough. Despite these gendered perspectives, many of the shared narratives highlighted similar themes of financial hardship, lack of educational opportunities and safety concerns around sexual and gender-based violence (SGBV). CONCLUSIONS: A complex myriad of factors contribute to early marriage including poverty, lack of educational opportunities and concerns about SGBV. Sexual exploitation under the guise of marriage is a reality for some Syrian girls. Gender-specific strategies to address child marriage might be more effective in reducing this harmful practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".