Bibliographic record
Abstract
In 2015, the United Nations International Children’s Emergency Fund (UNICEF) named Syria as the most dangerous place on earth to be a child (UNICEF, 2). Since the onset of civil war in 2011, nearly 4.8 million Syrians are refugees outside of Syria and approximately 6 million are internally displaced (United Nations Office for the Coordination of Humanitarian Affairs, 2016). While some refugees have successfully resettled in North American and European nations, many remain in limbo in refugee camps. What is most staggering about the population of affected persons is that nearly half, approximately 6 million, are children (UNICEF, 2016). Nearly all of these children have been subjected to trauma that has manifested in a variety of ways. They have often been subjected to or witnessed violence and have experienced the loss of one or more of their caregivers. Refugees face difficulty accessing psychological and health services and are met with the stigma surrounding mental health in countries including Lebanon and Turkey, regions that many refugee children have fled to. In the absence of these supports, the mental trauma a child is experience can impact learning and development and have disastrous impacts on their future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".