Blood, sweat and tears: On the corporeality of deportation
Bibliographic record
Abstract
It is hard to imagine how deportation regimes could function without the threat or the exercise of force. Yet surprisingly a focus on forces and bodies, and more generally the question of corporeality, has rarely been foregrounded by migration scholars looking at deportation. Academic study of clandestine border crossing as well as detention abounds with descriptions and theorization at the level of the body. Why not deportation? Building on fieldwork with cantonal police units in Switzerland between 2015 and 2017, this paper calls for scholars of deportation to take corporeality seriously. We follow some of the corporeal practices implemented by state actors and related experts and authorities to understand how bodies feature in removal practices in terms of senses, feelings, affects, nerves, pulses, breathing. Violence overarches this scene, but it is by no means the whole story in the state’s struggle for sovereignty and racialised removal, since we should equally register the other moves that are integral to deportation operations such as calming, monitoring, medicating, consoling, dressing, undressing, and inspecting. To overlook the corporeal is to risk producing an overly sanitized, cleansed, tidy depiction of deportation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.085 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".