Subhumanism: The re‐emergence of an affective‐symbolic ontology in the migration debate and beyond
Bibliographic record
Abstract
Abstract A critical analysis of social behavior proposes that the contempt for immigrants, refugees, or asylum seekers around the world is explicitly or implicitly powered by an ontology of the subhuman, a term that was used in early 20thcentury American race and eugenic theory, as well as in fascist Germany, to describe and justify the mistreatment of minorities or perceived enemies. “Migrants” are not afforded the same rights and respect as other people, because they are not conceived as real humans, and their subhuman status allows them to be understood as criminals, degenerates, and even parasites, which are infesting the orderly body of the nation. Subhuman theory works with affects rather than with theoretical analyses, with visualizations and imaginations instead of intellectual concepts, and with a normalized, manufactured common sense. It is argued that at the core of the subhuman lies the idea of chaos, unhealthy appearance, and disorderly behavior, from which humanity is removed. Discussed are processes of subhumanization, the relationship between subhumanism and racism as well as fascism, and the dialectics between the particular and the general, which proposes a shared world for all humans. It is suggested that psychological concepts are limited, and that political, legal, and resisting action is required to combat the re‐emergence of a normalized ontology.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.085 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".