“They Stopped the Lives of Others”: Stateless Palestinians Facing Bureaucratic Violence in Sweden
Bibliographic record
Abstract
Official calls for “failed” asylum seekers to leave Sweden ignore the difficulties and harms befalling stateless people who cannot return to previous countries of residence because they lack citizenship. Stateless people are caught in limbo, a position where they have no prospects of return or of attaining a residence permit in a predictable future. To learn the underlying logics and consequences of such limbo and how it is (re)produced in the Swedish migration bureaucracy, this article investigates three data sets: interviews with seven stateless Palestinians, the Swedish Migration Agency’s internal guidelines for the return process, and the same agency’s country reports on stateless people’s situation in the assigned deportation countries. Inspired by Hannah Arendt’s reflections on statelessness and modern bureaucratized societies, the article reveals that there are great challenges to access rights for stateless persons and in holding anyone accountable for decisions adopted by Swedish migration authorities. Moreover, the article shows how limbo induces two interconnected and multilevel technologies in migration authorities: ignorance and repressive consent. As communicating vessels, these technologies form a bureaucratic violence. While diminishing migrants’ access to safety and a dignified life, violence is sustained by legislative changes and insidiously hidden from public debate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".