Diasporas from the Middle East: Displacement, Transnational Identities and Homeland Politics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Migrants and refugees from Middle Eastern countries are scattered around the globe, predominantly in the MENA (Middle East and North Africa) Region, Europe and the USA. Between 2005 and 2015, the number of migrants living in the Middle East more than doubled, from about 25 million to around 54 million.1 Some of this growth was due to individuals and families seeking economic opportunities. But the majority of the migration surge, especially after the war in Syria began in 2011, was a consequence of armed conflict and the forced displacement of millions of people from their homes, many of whom have left their countries of birth.2 Furthermore, the estimated number of immigrants to Europe between mid-2010 and mid 2016 was 7 million, not including 1.7 million asylum seekers. Among these European countries, Germany recorded the highest level of immigration, followed by Britain, France, Spain and Italy.3 These migration flows not only reflect the existence of drivers of migration due to conflict in the Middle East, but also reveal the potential formation of new diasporas throughout time or growing size of the already existing ones in host countries all around the world. Mobilization has also taken place also in online platforms, thanks to the new communication technologies and easy access to homeland media outlets. The technological revolution transformed the experiences of refugees throughout the stages of their journey: premigration, in transit and in the new surroundings. Millions of refugees from the Middle East use smart phones and social media applications to receive information about the host states, as a survival tool during the escape process, navigate border crossings, and to receive information on political situations and the possibilities of return.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it