Bibliographic record
Abstract
This study aimed to identify the kinds of challenge encountered by Syrian refugee children who are living in Jordan but not in refugee camps. A sample of 120 Syrian refugee children residing in Amman provided the data for this study, which is based on a descriptive approach. The Statistical Package for the Social Sciences (SPSS) was used to analyze the data. We found that the main economic challenges faced by these children were low wages, labor exploitation, difficulties with paying back debts for their families, ongoing poverty, and the high cost of living in Jordan. Educational challenges were also largely economic and were mainly due to the high cost of education and the priority of work over school attendance. Health challenges too were economic and centered on the high cost of health care and the obstacles to obtaining medical insurance. Social challenges included lack of interpersonal bonds, an inability to form new friendships, and the absence of entertainment. This study suggests that providing financial support for Syrian refugee families consistent with the increasing cost of living in the hosting country would result in better lives for the Syrian children, as would creating job opportunities for heads of families in line with memoranda of agreement that Jordan has with international organizations. Further, public education for Syrian refugee children should be made free of charge, particularly in the elementary stages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".