Thinking clearly about causal inferences of politically motivated reasoning: Why paradigmatic study designs often undermine causal inference
Bibliographic record
Abstract
A common inference in behavioral science is that people’s motivation to reach a politically congenial conclusion causally affects their reasoning—known as politically motivated reasoning. Often these inferences are made on the basis of data from randomized experiments that use one of two paradigmatic designs: Outcome Switching, in which identical methods are described as reaching politically congenial versus uncongenial conclusions; or Party Cues, in which identical information is described as being endorsed by politically congenial versus uncongenial sources. Here we contend that these designs often undermine causal inferences of politically motivated reasoning because treatment assignment violates the excludability assumption. Specifically, assignment to treatment alters variables alongside political motivation that affect reasoning outcomes, rendering the designs confounded. We conclude that distinguishing politically motivated reasoning from these confounds is important both for scientific understanding and for developing effective interventions; and we highlight those designs better placed to causally identify politically motivated reasoning.
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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 | 0.456 | 0.686 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".