Matryoshka Journeys: Im/mobility During Migration
Bibliographic record
Abstract
Acts of mobility require corresponding acts of immobility (or suspended mobility). Migrant journeys are not only about movement. Indeed, in the present policy context, this is ever more true. Whether a migrant is contained within a hidden compartment, detained by migration authorities, waiting for remittances to continue, or marooned within a drifting boat at sea, these moments of immobility have become an inherent part of migrant journeys especially as states have increased controls at and beyond their borders. Migrants themselves view this fragmentation – the stopping, waiting and containment – as part of the journey to be endured. Drawing on the authors’ fieldwork in Central America and Southern Europe, this paper destabilises the boundary between transit and settlement, speaking to a larger policy discourse that justifies detentions and deportations from the United States and countries on the periphery of Europe. We argue that migrants’ nested experiences of these ‘matryoshka journeys’ reveal how increased migration controls encourage them not only to take greater risks during the journey, but also to forfeit their agency at opportune moments. In turn, states exploit images of such im/mobility during the journey in order to emphasise the irrational risks migrants take in order to traverse seas and deserts and to cloak their own border policies in a humanitarian discourse of rescue.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".