Return and Retreat in a Transnational World: Insights from Eritrea
Bibliographic record
Abstract
When refugees’ access to economic, political, and social rights cannot be guaranteed in one locale, individuals make pragmatic choices about what relationships to sustain with authorities elsewhere, even with those that caused their flight in the first place. This process of return is rarely akin to conventional repatriation, understood as the full re-establishment of the rights and responsibilities associated with citizenship (Bradley, 2013). In this paper, the authors instead propose the concept of retreat to capture the process initiated by those who are seeking to escape protracted displacement through a partial return to their country of origin, and through which individuals hope that they can assemble multiple sources of rights across several locations. Drawing from recent ethnographic research in Eritrea, the authors analyze the stories of individuals, mostly refugees, who have decided to retreat despite the lack of political change. Neither exclusively citizens nor refugees in countries of origin or asylum, research participants’ “dually absent” socio-legal position is analyzed in this article. The authors show that this rests on stratified forms of citizenship and the relational nature of different rights and statuses and argue that this position should be recognized as an additional dynamic in the literature on flight, return, and transnational citizenship.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".