Syrian and Palestinian Syrian Refugees in Lebanon: the Plight of Women and Children
Bibliographic record
Abstract
The humanitarian crisis resulting from the Syrian conflict is estimated to be the worst so far of this century. The recent influx of refugees has now reached a point where they are equal to one quarter of Lebanon’s population, causing evident strains on its fragile economy and social structure. Syrians in Lebanon have fled from their home to seek safety, however their vulnerability is now in question as women’s and children’s rights continue to be under threat. This paper investigates the plight of Syrian and Palestinian Syrian refugees in Lebanon with an emphasis on women and children. While there are many issues confronting refugees in Lebanon, a thorough examination of this is beyond the scope of this paper. Therefore, an examination of the two most prominent issues surrounding education and violence is conducted. More specifically, this paper exposes and discusses children’s access to education in Lebanon and the short and long term effects of children forgoing education, both as economic setbacks, the lack of educated people to rebuild Syria and how education is linked to a reduction in violence against women . It will further discuss the shift in the violence that women and children are exposed to, highlighting the increase in violence that they are experiencing. The main forms of violence are manifesting in Intimate Partner Violence, early marriage, survival sex, and the threat and fear of violence from the local community
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".