Cultural artefacts and the ‘migration crisis’: Disruptive materialities in works by Navid Kermani and Maxi Obexer
Bibliographic record
Abstract
Amplified worldwide fragility and growing mobility have contributed to increased forced migration towards Europe. However, Europe’s present focus on border protection has furthered the ‘migrant crisis’ which is very much a crisis of response. News about the ‘migrant crisis’ continues to dominate political discourse in Europe and elsewhere. The discussions typically focus on Europe’s supposed solutions in the form of increased border security, new political agreements, and various forms of humanitarian aid. This article reviews four literary texts about Europe’s responses to forced migration and proposes that the literary treatment of various cultural artefacts employed in these texts critiques Europe’s current restrictionism. Two speeches by Navid Kermani, ‘Towards Europe’ and ‘On the sixty-fifth Anniversary of the Promulgation of the German Constitution’ and two novels by Maxi Obexer, Wenn gefährliche Hunde lachen (‘When dangerous dogs laugh’) and Europas längster Sommer (‘Europe’s longest summer’) make reference to several phenomenal objects and also to gestures. In and of themselves, these cultural artefacts such as beds, blankets, buses, lipsticks, T-shirts, shoes, and even the gestures of kneeling and bowing, may not possess anything disruptive. However, there is an unruly quality about them that puts a spotlight on the precarity of survival migrants who cannot access the European asylum process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".