The Reasonable, the Rational, and the Good: On Folk Theories of Deliberative Judgment
Bibliographic record
Abstract
Abstract Judgment is often described in terms of an intuitive (System 1) versus deliberative (System 2) dichotomy, yet sound deliberation itself can take more than one form. Building on philosophical traditions and distinctions in treatment of sound judgment in economics and law, we propose that lay conceptions revolve around two distinct types of deliberate judgment: rational, emphasizing rule-based and utility-focused reasoning for well-defined problems, and reasonable, prioritizing context-sensitive and socially conscious reasoning for ill-defined problems. Across four studies in English-speaking Western samples (Studies 1–4; N = 2,130) and a Mandarin-speaking Chinese sample (Study 4; N = 697), participants described their notions of “sound” and “good” judgment, evaluated social scenarios, chose between candidates with distinct judgmental profiles, and categorized non-social objects. Results consistently showed that people view both rationality and reasonableness as common forms of deliberate sound judgment, while treating them as distinct. Participants preferred rational deliberation for algorithmic social roles linked to well-defined tasks and reasonable deliberation for interpretive roles linked to ill-defined tasks. Moreover, framing decisions as rational vs. reasonable influenced whether participants relied on rule-based vs. overall-similarity strategies in classification tasks. These findings suggest that lay understanding of sound judgment does not rely on a single standard of judgmental competence. Instead, people recognize that both rationality and reasonableness are critical for competent deliberation on different types of problems in life.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.065 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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; 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".