Genetic essentialism: On the deceptive determinism of DNA.
Bibliographic record
Abstract
This article introduces the notion of genetic essentialist biases: cognitive biases associated with essentialist thinking that are elicited when people encounter arguments that genes are relevant for a behavior, condition, or social group. Learning about genetic attributions for various human conditions leads to a particular set of thoughts regarding those conditions: they are more likely to be perceived as (a) immutable and determined, (b) having a specific etiology, (c) homogeneous and discrete, and (d) natural, which can lead to the naturalistic fallacy. There are rare cases of "strong genetic explanation" when such responses to genetic attributions may be appropriate; however, people tend to overweigh genetic attributions compared with competing attributions even in cases of "weak genetic explanation," which are far more common. The authors reviewed research on people's understanding of race, gender, sexual orientation, criminality, mental illness, and obesity through a genetic essentialism lens, highlighting attitudinal, cognitive, and behavioral changes that stem from consideration of genetic attributions as bases of these categories. Scientific and media portrayals of genetic discoveries are discussed with respect to genetic essentialism, as is the role that genetic essentialism has played (and continues to play) in various public policies, legislation, scientific endeavors, and ideological movements in recent history. Last, moderating factors and interventions to reduce the magnitude of genetic essentialism, which identify promising directions to explore in order to reduce these biases, are discussed.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.050 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".