Syrian trajectories of exile in Lebanon and Turkey: Context of reception and social class
Bibliographic record
Abstract
Abstract Research on refugee trajectories rarely takes into account social class. Yet migration options for those fleeing conflicts and insecurity are more or less dangerous, desirable and doable and differences in trajectories have to do with people's ability to mobilise resources at a given time and in a given space. Based on 42 semistructured interviews conducted with Syrians who fled to Lebanon and Turkey between 2012 and 2017, this article focuses on how trajectories of exile are shaped by the interactions between social class and the specificities of the context of reception. These countries are similar to the extent that they both border Syria and host the largest numbers of Syrian refugees worldwide. Yet their respective policies, as well as their unique historical, legal and socio‐economic contexts, create different impacts on refugee trajectories and refugees' experience of inequality. We argue that as a result of the different contexts of reception for Syrians in Lebanon and Turkey, refugees' strategies to reduce precarity also differ significantly. As such, the relevance and usefulness of any given form of capital vary. Results indicate that survival strategies and mobilisation of capital in Lebanon revolve around obtaining legal status, whereas in Turkey, where all Syrian refugees benefit from legal status under the temporary protection regime, capital is mainly used to negotiate an exploitative labour market. Our study challenges conventional class analyses that imply direct links between the possession of economic and social capital and favourable trajectories of exile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".