The Importance of Sport in Integratıng Refugees Into Germany
Bibliographic record
Abstract
Germany hostsabout 7.4 millionimmigrants, thelargestethnicgroup (26.6%) is Turkish. Inrecent times, due to Iraq War in the 1990’s and the start of the war in Syria in 2012, people fled from their homelands to Turkey. Some of them stayed, while others travelled to Greece in order to ask asylum in Europe. Nowadays, about 5 million Syrians live in Syrian neighbour countries; 3.4 million Syrians are settled in Turkey, 1 million in Lebanon, more than half a million in Jordan and a quarter of a million in Iraq. It has been reported that 150.000 Syrians settled in Northern African countries. The number of Syrians living in neighbouring countries is estimated to be about 41% of the original population. About 1 million of the Syrian population has been living in Europe, about a half of are settled in Germany, which means that Germany hosts in absolute numbers the highest number of Syrians refuges amongst all European countries. The high and sudden rise of the number of refugees in Germany caused some serious social problems and tensions in the society. Integration of migrants in German culture and society started to become an important issue of (local) authorities and institutions. In this context sport can be considered as an important instrument as sport generally does not seem to suffer from prejudices in the context of race, colour and language. In 1989 The Deutsche Associations of Olympic Sports with the initiative of the Federal Government started the so called ‘Concretion and Integration through Sport’ programme. This project aimed to inspire members of sports clubs and volunteers to encourage minority ethnic groups’ participation in sport activities. Due to this increasing participation in multi ethnic groups, the community’s social cohesion improved. The participation of sport members in multi ethnic groups encouraged and developed open mindedness to other cultures, intercultural awareness and mutual respect.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".