How messages about behavioral genetics research can impact on genetic attribution beliefs
Bibliographic record
Abstract
<ns4:p>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.</ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".