Mediating atmospheric bordering: Migratory journeys in hostile environments
Bibliographic record
Abstract
Abstract Many displaced people experience interminable vulnerability, protracted waiting, and persistent frictions and uncertainties during their migratory journeys to seek protection. These journeys also comprise little‐studied practices that displaced people use to mediate the atmospheres of hostile border environments. I refer to these practices as ‘mediating atmospheric bordering’. The paper contributes to conversations on affective atmospheres and borderings in critical migration and border studies. It focuses on displaced people's border engagements in migratory journeys, and the emotional experiences they encounter and how they exert agency. The main claim of the paper is that displaced people mediate affective processes that aim to shape their movements, and that such mediations are critical for understanding: the importance of sensory relations and spaces of movement that might otherwise remain obscure; the ways to challenge affective politics; and the relationship among migratory journeys, agency, and social networks. The empirical analysis draws on policies, programmes, and scholarly materials and a selection of 65 interviews with formerly displaced Syrians in Canada and in Sweden on their migratory journeys within and outside of Syria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".