“The” Interpretation(s) of Conditionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent studies indicate that a vast majority of people judge the probability of a conditional <if A then C> as equivalent to the conditional probability of <A, given C>. This means that in evaluating the applicability of a conditional people do not seem to take into account situations in which the antecedent <A> is false. This has been taken as evidence against the model theory of Johnson-Laird and Byrne (2002). This theory, however, claims that the conditional interpretation in which false-antecedent cases are relevant is only one of many possible interpretations of "if." We present new evidence that confirms this flexibility of the interpretive system. When people are primed by thinking (1) about truth and the difference between the <if A then C> and <if A then possibly C> or (2) are invited to judge which situations are consistent with the conditional, they are more likely to select a probability estimate that takes into account the false-antecedent cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it