Understanding Iranian Refugee Discourse in Turkey on Twitter by Using Social Network Analysis
Bibliographic record
Abstract
The problem of asylum seekers is still a major concern worldwide, including for the transiting countries like Turkey, which hosted more than 4 million asylum seekers in its territory. The main question is, how long is this going to continue? To answer that question, understanding what Iranian asylum seekers want is also very important, in order to define a long-term solution. Based on the data analysis in the paper, most of the refugees who have already settled in Turkey for more than 3 years, mostly want to join the programme of resettlement to several refugee recipient countries, such as Canada and the European Union (EU), conducted by the UNHCR. However, there are some obstacles for them to successfully be admitted in the resettlement programme. First, the acceptance rate is very low, under 5% yearly, and the required processing time is sometimes more than five years. The proposed solution is to open access to education for Iranian refugees and implement the EU Blue Card for the refugees and asylum seekers to resettle them voluntarily to EU territory. Hopefully, that legal solution can become a long-term solution for Iranian asylum seekers and refugees who are to this day still struggling in the Turkish territory.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".