Challenges to the integration of Syrian refugees
Bibliographic record
Abstract
In this study, we provide an overview of the situation of Syrian refugees and other non-citizens living in host countries. We explored the cases of several countries: Turkey, which is one of the main destinations for refugees, flees the Syrian crisis; Germany and the United Kingdom, which are high-income countries where the public sentiment about refugees has changed over time; Greece and Italy, nations that share a close border with countries experiencing large refugee influxes; and Canada and Australia, which have had different approaches regarding inflows of refugees and do not share borders with countries that have significant outflows of migrants and refugees. Our review of policies suggests that the successful resettlement of Syrian refugees is dependent on political commitment that is coupled with public support and community engagement. Successful social and economic policies to address 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 request of non-profit organizations and academic institutions.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".