Through the Localization Looking Glass: Seeing Subaltern Power in the Refugee Regime
Bibliographic record
Abstract
Abstract There has been increased scholarly and policy attention to “localized” responses to displacement, in the hope that further empowering local actors may unlock new means of protecting refugees’ rights and addressing their needs. However, these efforts have often oversimplified power relations within localization processes, bringing some players into focus while occluding others, and devoting insufficient attention to how localization processes and the power dynamics surrounding them have evolved over time. In response, this article draws on theories of subalternity and subaltern agency from the field of postcolonial studies to develop a more nuanced conceptualization of power in localization processes in the refugee regime. We contend that subalternity is best understood as a fluid, relational position that changes over time, such that particular refugees and displaced groups may oscillate between dominant and marginalized, subaltern subject positions, within intersecting systems of power. We probe refugees’ subaltern agency in terms of resistance and persistence, and deepen this account through analysis of localized responses to Burundian refugees in Tanzania, focusing on the localization of efforts to secure durable solutions for refugees. We argue that localization scholarship, particularly in the context of the refugee regime, needs to move beyond homogenized, dehistoricized, and romanticized notions of grassroots, refugee-led responses and focus on complex and fluid power configurations among diverse local actors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".