Species Metaphors and Biopolitics in Contemporary Novels of Forced Migration
Bibliographic record
Abstract
On the populist front the language and imagery used for migrants reinforce the perception of them as trespassers and criminals, implying fears of their appearance in large numbers or as lone wolf terrorists. The German right-wing party AfD compares the recent appearance of wolves on German soil with what it perceives as trespassing migrants; Trump used to refer to migrants from the global South as “animals”, labelling them as “predators and invaders infesting America”; David Cameron famously invoked the images of biblical locust infestations by comparing migrants with “swarms of people coming across the Mediterranean”; and UK media pundit Katie Hopkins compared migrants with “feral humans” and “cockroaches”. It is a rhetoric that has become an integral part of what Fintan O’Toole has described as the “new pre-rather than old post-fascist” political climate. In my essay I will discuss three contemporary novels, Francisco Cantú’s The Line Becomes a River (2018), Norbert Scheuer’s Winterbienen (Winter Bees, 2019), and Rawi Hage’s Cockroach (2008) to demonstrate how these authors instrumentalize three species metaphors – wolves, bees, and cockroaches – for a literary representation of forced migration. Their works show us how fluid such metaphors are and how these authors reconfigure three species metaphors and redeem them from former cultural and political representations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".