Bibliographic record
Abstract
Abstract Poverty, political turmoil, armed conflict, and human trafficking are but a few factors that lead to the significant migration of children. Researchers claim that the burden of ill health, infection, and emotional disturbance is much higher in child migrants than in other children (Hjern & Bouvier 2004). More than one‐quarter of refugee children in the UK are believed to have significant psychological disturbances (Fazel & Stein 2003). Scandinavian studies of refugee children indicate that 40 to 50 percent of children in asylum‐seeking families suffer from psychiatric and psychosomatic symptoms (Ekblad 1993; Almquist & Brandell 1997; Hjern et al. 1998). Almost all subjects (94%) among a group of internally displaced Bosnian children fulfilled the criteria for post‐traumatic stress disorder (PTSD) (Goldstein et al. 1997). Similar findings were reported about Sudanese refugee children in Uganda (Paardekooper et al. 1999). Rates of PTSD varying from 11.5 to 28 percent were found in refugee children from Tibet and Bosnia (Weine et al. 1995; Servan‐Schreiber et al. 1998). Children who experienced war in Cambodia and former Yugoslavia reportedly had PTSD prevalence rates of 40 to 50 percent upon resettlement in the US (Weine et al. 1995; Servan‐Schreiber et al. 1998; Papageorgiou et al. 2000).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".