<i>Refugees and citizens</i> : Understanding Eritrean refugees’ ambivalence towards homeland politics
Bibliographic record
Abstract
This article revisits ambivalence as a protracted state which does not simply develop as a result of the migration experience but stems from overlapping levels of normative inconsistency. Drawing from my ethnography of Eritreans’ everyday life in the homeland and abroad, I analyse their attitudes of patriotism and disenchantment through an ambivalence lens. Their ambiguous attitudes are arising from national and transnational Eritrean state policies and are further complicated by their role as “political refugees” in host countries. My informants’ ambivalence stems from them embodying more than one role (i.e. patriots, family breadwinners, refugees from and citizens of their homeland), from contradictory expectations pertaining to the same role (i.e. young citizens in Eritrea) and from clashing implications of being members of two different social systems (i.e. the destination country and the country of origin). Thus, Eritreans’ political loyalties and actions are characterised by a state of ambivalence throughout their migration process. Despite its peculiar characteristics, this case study sheds light on the complexity of ambivalence, as more than a temporary condition, for migrants and refugees in particular. In the current scenario of emigrant states’ transnational governance, protracted ambivalence is likely to mark the attitudes of an increasing number of people on the move as both refugees from and citizens of their country of origin.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".