Dynamics of mobility-stasis in refugee journeys: Case of resettlement from Turkey to Canada
Bibliographic record
Abstract
Abstract The refugee Odyssey is often not a linear, straightforward movement from point A to point B, from sending country to receiving one. Rather, it involves multiple paths, gateways, entry and exit points, and territories en route to the country of resettlement. Crucially, the journey involves not only mobility but also immobility and/or periods of stasis—breaks that are, in many cases, a natural part of the journey. Alongside this diversity of paths and movements, the refugee experience—understood in terms of the practices and acts of refugees en route—is also far from homogeneous. Each journey may well have an episodic character, where the course, direction, and periods of waiting for one asylum traveller can differ significantly from those of previous and/or future travellers—even if the departure point and destination are the same. Within this context, this article examines the breaks or periods of stasis that punctuate the refugee Odyssey, which we call mobistasis. We base our empirical findings on research conducted with people en route to resettlement in Canada via Turkey where they initially seek asylum and await resettlement. Drawing on fieldwork in Turkey and Canada between April 2014 and October 2016 and semi-structured interviews conducted with asylum travellers from non-European countries, the article illustrates how Turkey as the country of asylum is more than a space of mere ‘transit’. It rather constitutes a space of mobistasis—stasis within movement—in the asylum voyage towards countries of resettlement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".