The uncanny return of the race concept
Bibliographic record
Abstract
The aim of this Hypothesis and Theory is to question the recently increasing use of the "race" concept in contemporary genetic, psychiatric, neuroscience as well as social studies. We discuss "race" and related terms used to assign individuals to distinct groups and caution that also concepts such as "ethnicity" or "culture" unduly neglect diversity. We suggest that one factor contributing to the dangerous nature of the "race" concept is that it is based on a mixture of traditional stereotypes about "physiognomy", which are deeply imbued by colonial traditions. Furthermore, the social impact of "race classifications" will be critically reflected. We then examine current ways to apply the term "culture" and caution that while originally derived from a fundamentally different background, "culture" is all too often used as a proxy for "race", particularly when referring to the population of a certain national state or wider region. When used in such contexts, suggesting that all inhabitants of a geographical or political unit belong to a certain "culture" tends to ignore diversity and to suggest a homogeneity, which consciously or unconsciously appears to extend into the realm of biological similarities and differences. Finally, we discuss alternative approaches and their respective relevance to biological and cultural studies.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.069 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".