How messages about behavioral genetics research can impact on genetic attribution beliefs
Bibliographic record
Abstract
Science communication has the potential to reshape public understanding of science. Yet, some research findings are more difficult to explain and more likely to be misunderstood. The contribution of this paper is threefold. It opens with a review of fascinating interdisciplinary literature on how scientific research about human genetics is disseminated in the media, and how this type of information could influence public beliefs and world views. It then presents the theoretical framework for my research program, providing a logical basis for how messages about human genetics may influence people's beliefs about the role of genes in causing human traits. Based on this reasoning, I formulate the genetic interpolation hypothesis, which predicts that messages about specific research findings in behavioral genetics can lead members of the public to infer greater genetic causation for other social traits not mentioned in the content of the message. While this framework offers clear, testable predictions, some questions remain unaddressed. For instance, what kind of message formats are persuasive enough to alter people's views? The third contribution of this paper is to begin to address this question empirically. I present the results of a survey experiment that was designed to test whether a simple, short paragraph about behavioral genetics is a powerful enough stimulus to cause the genetic interpolation effect.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".