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Record W3006613007 · doi:10.1111/jtsb.12237

Subhumanism: The re‐emergence of an affective‐symbolic ontology in the migration debate and beyond

2020· article· en· W3006613007 on OpenAlexafffund
Thomas Teo

Bibliographic record

VenueJournal for the Theory of Social Behaviour · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHumanityRefugeeSociologyDialecticRacismOntologyContemptPoliticsEpistemologyRace (biology)ImmigrationSocial psychologyPsychologyGender studiesLawPhilosophyPolitical science

Abstract

fetched live from OpenAlex

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 20 th century 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.348
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2020
Admission routes2
Has abstractyes

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