Challenges for the integration of Syrian refugees
Bibliographic record
Abstract
We conducted a study to provide an overview of the situation of Syrian refugees and other non-citizens living in host countries, as well as to summarize a series of policies and legislation towards refugees. We explored the cases of: (1) Turkey, which is one of the main destinations for Syrians fleeing the crisis in their home country; (2) Germany and United Kingdom, high-income countries where the public sentiment about refugees has changed/shifted overtime; (3) Greece and Italy, countries that share a close border with countries from where there are large refugee influxes; and (4) Canada and Australia, which do not share borders with countries from which there is a significant refugee influx and have had some success with integrating migrants and refugees. Our review of refugee policies suggests that successful resettlement of Syrian refugees was mainly due to political commitment coupled with an incredible public support and community engagement, including private sponsorship of refugees. Successful social and economic policies to deal with the refugee crisis demand a combined effort in terms of planning, implementing, monitoring, and assessing initiatives. Most importantly, record keeping and sharing data with stakeholders need to be improved, which is a joint complaint by non-profit organizations and academic institutions.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| 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".