Reasoning from or reasoning about beliefs: Truth-based or possibility-based compatibility judgments and Handley et al.’s (2006) litmus test of the suppositional conditional
Bibliographic record
Abstract
How do people go about in evaluating the consistency of their beliefs? Do people reason from or do they reason about their beliefs in judging whether they are compatible, or both? The paper investigates how people evaluate belief in contrary conditionals and . Experiment 1 (N=141) indicates people do not use a notion of possibility-based compatibility according to which claims are compatible when they share a common possibility: After being given this definition, there was no improvement of compatibility judgments even though more than 60% confirmed a shared possibility. Experiment 2 (N = 95) and Experiment 3 (N=93) test the alternative truth-based notion of compatibility, according to which contrary conditionals are incompatible because they cannot be true at the same time. The sets people construct for a true conditional invariably include “A and C” cases (Experiment 2) and “if A then C” is judged “un-assertable” about sets that do no include such cases (Experiment 3). Such “A and C” cases are at the same time impossible when "if A then not-C" is true. Findings thus suggest contrary conditionals are judged incompatible because they cannot be true at the same time.
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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 | 0.012 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".