An evaluation of the association between participant sex and perceptions of child marriage among Syrian refugees in Lebanon
Bibliographic record
Abstract
Since the onset of war in Syria, the occurrence of child marriage has risen from 13% in pre-war Syria, to approximately 35% among Syrian refugee girls. Economic instability, societal norms, and safety concerns all contribute to this practice. This study examines the underlying causes of child marriage by analyzing the association between participant sex and perceptions of child marriage among Syrian refugees in Lebanon. Data were obtained from a cross-sectional, mixed-methods study conducted in Lebanon in 2016. Multivariate logistic regression was used to evaluate the association between participant sex and attributing child marriage to safety concerns or, independently, attributing child marriage to financial insecurity. We analyzed 560 micronarratives, 52.1% of which were provided by women, with the majority of participants being under the age of 35 (78.2%). Men were found to have lower odds of attributing child marriage to safety concerns (OR = 0.54, 95% CI = 0.29-0.98), and higher odds of attributing child marriage to financial insecurity (OR = 1.76, 95% CI = 1.06-2.92), compared to women. When stratified by location in Lebanon, men in Tripoli had a higher odds of attributing child marriage to financial insecurity (OR = 2.68, 95% CI = [1.11, 6.50]). Given these differences in perceived reasons for child marriage between men and women, gender-specific messaging and initiatives could be utilized to address the underlying issues that lead to child marriage, and reduce the occurrence of this practice, particularly in Tripoli.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".