Measuring Rational Thinking in Adolescents: The Assessment of Rational Thinking for Youth (ART‐Y)
Bibliographic record
Abstract
ABSTRACT There has been considerable conceptual and empirical progress on the measurement of rational thinking in adult samples. Studies in developmental samples have demonstrated that many of these domains and paradigms can also be assessed in children and youth, especially in adolescent samples. Here, we present an efficient rationality assessment battery for adolescents and youth—the Assessment of Rational Thinking for Youth (ART‐Y). The ART‐Y consists of five subtests: Probabilistic and Statistical Thinking, Scientific Thinking, Avoidance of Framing, Knowledge Calibration, and Rational Temporal Discounting. Two supplementary measures of thinking dispositions are included in the ART‐Y: Actively Open‐Minded Thinking (AOT) and Deliberative Thinking. The ART‐Y battery was examined in a sample of 143 adolescents (mean age = 15.4 years). The five rational thinking subtests displayed intercorrelations largely consistent with those obtained in the adult literature. Age, cognitive ability, problem solving, probabilistic numeracy, and thinking dispositions predicted variance differently across the five subtests of the ART‐Y, but again largely consistent with the adult literature. These measures, along with the ART‐Y subtests, were examined as predictors of two real‐world skills: financial literacy and academic achievement. Scientific thinking, knowledge calibration, and rational temporal discounting were significant unique predictors of financial literacy when statistically controlling for cognitive ability. Scientific thinking predicted academic achievement when statistically controlling for cognitive ability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".