Impairment and Traumatization as Crucial Factors for Didactics and Pedagogy of Adolescent Refugees
Bibliographic record
Abstract
Discussions about the extent of integration of adolescent refugees as well as integrating displaced people ‘correctly’ in the respective education system of the target country have long been held about. Since crisis caused by, for example, war or the Covid-19 pandemic increase the numbers of refugees all over the world, a high number of displaced people suffer from experienced traumas and might therefore be impaired in participating in curricular education. For this study, the observation of three adolescent refugees who attend different sports classes, thus being encompassed by variable social settings, has been at the center of attention for one semester. To ensure data variety, principals, PE teachers, refugee students, ‘regular’ students have been interviewed, respectively. The analysis revealed that huge differences in terms of adaptation as well as impairment could be observed among the participating adolescent refugees. While one of the refugee students easily adapted among the observed manifestations (non-)verbal communication, social form, behavioral strategies and potentials of physical education, the other adolescent refugee displayed severe impairment in all of the manifestations mentioned above; hence, experienced traumata experienced before, during or after flight require newly arriving students being psychologically examined and monitored.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".