Bibliographic record
Abstract
Since 2011, there has been an ongoing civil war in Syria between various militant groups, ISIS, and the Syrian government, in response to the oppressive regime of the Syrian government of President Bashar al-Assad. As a result, the largest migration that the world has seen since the Second World War has transpired. Approximately 13 million Syrians have been forcefully displaced from their homes, making this one of the largest humanitarian crises of our time. Many Syrians have sought refuge in neighbouring countries, as well as in Europe, the United States, and Canada. There is notably little research on refugee adaptation in Europe, which is the focus of this study. Using aspects of the Multidimensional Individual Differences Acculturation (MIDA) model, this study looked to examine the sociocultural and psychophysical adaptation of Syrian refugees in Germany. Measures that were excluded from the current version of the MIDA model were Ingroup Contact and Outgroup Contact. Researchers at Ludwig Maximilians University Munich administered paper and pencil surveys to 265 participants in Nuremberg, Germany who were attending vocational and language schools. Results displayed a significant relationship between Psychosocial Resources and Integration, and Psychophysical Distress; Co-National Connectedness and Integration; and Hassles and Psychophysical Distress. This study looks to inform host country government policies about positive integration strategies for refugee adaptation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".