Actively Open-Minded Thinking, Bullshit Receptivity, and Susceptibility to Framing
Bibliographic record
Abstract
Abstract. The framing effect occurs when different presentations of the same problem lead to predictably different preferences. The dual-process framework of higher cognition assumes that the effect violates rational principles, but alternative accounts and recent evidence have contested this interpretation. Contributing to this debate, we tested the dual-process assumption by investigating associations between susceptibility to framing and the willingness and ability to think in line with rational norms, conceptualized as actively open-minded thinking and pseudo-profound bullshit receptivity. We conducted two online studies among North American ( N = 259) and Bulgarian ( N = 248) university students and administered several framing problems within-subjects, presumably necessary for the associations to appear. Confirmatory factor analyses showed that susceptibility to framing was associated with decreased actively open-minded thinking and increased bullshit receptivity in both sites. Exploratory multi-group analyses demonstrated partial strong invariance and showed that the findings generalize across both sites in terms of direction and partially in terms of magnitude. These results broadly support the dual-process account of the framing effect. Our study further contributes to adapting existing measures to a novel setting and expanding the findings across borders and populations.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".