Differentiating Theories from Evidence: The Development of Argument Evaluation Abilities in Adolescence and Early Adulthood
Bibliographic record
Abstract
An argument evaluation inventory distinguishing between different levels of theory-evidence differentiation was designed corresponding to the levels of argument observed in argument generation tasks. Five scenarios containing everyday theories about a social problem, and arguments to support those theories were presented to 170 participants from two age groups (15 and 22 years) and different educational tracks. Participants had to rate the validity of arguments proposed by a story figure, to support the theory, to choose the best argument, and to justify their choice. The rating task proved to be very difficult for all age groups, with only 49% of the university students consistently rating valid evidence-based arguments higher than flawed arguments. Competence improved with age and educational level. In the choice task more than 80% of the adults preferred an argument that reflected theory-evidence differentiation over mere theory elaboration or flawed reasoning. However, only adults with a university education were able to also explicitly justify their choice. Overall, these findings imply that laypersons have similar conceptual problems in differentiating theory from evidence as it has been reported for evidence generation tasks (Kuhn, 1991). Performance on the choice task suggests that some implicit awareness of differences between theory and evidence may precede a full, explicit understanding. Implications for education are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".