Bibliographic record
Abstract
In traditional tasks of formal reasoning, participants are asked to evaluate the validity of logical arguments. While this research tradition has contributed in many ways to our understanding of human reasoning, the extent to which this body of research generalizes to everyday, or informal, reasoning is unclear (e.g., Evans & Thompson, 2004; Galotti, 1989). The main goal of this dissertation was to illustrate the benefits of applying an informal approach to the study of conditional reasoning. In six experiments, everyday conditionals in the form of inducements (promises and threats) and advice (tips and warnings) were investigated. The results support three main conclusions. First, people recruit a substantial amount of background knowledge when interpreting and reasoning with these conditionals. Specifically, inducements were found to be different from advice on several pragmatic variables (Experiment 1); these variables also predicted differences in inference patterns (Experiment 2). Second, these studies provide further support for a probabilistic interpretation of conditionals (e.g., Evans & Over, 2004; Oaksford & Chater, 2001). Thus, in Experiments 3-5, estimates of different conditional probabilities
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.832 | 0.621 |
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".