Middle School Students’ Approaches to Reasoning about Disconfirming Evidence
Bibliographic record
Abstract
This study investigated differences in how middle school children reason about disconfirming evidence. Scientists evaluate hypotheses against evidence, rejecting those that are disconfirmed. Although this instant rationality propels empirical science, it works less for theoretical science, where it is often necessary to delay rationality – to tolerate disconfirming evidence in the short run. We used behavioral measures to identify two groups of middle-school children: strict reasoners who prefer instant rationality and quickly dismiss disconfirmed hypotheses, and permissive reasoners who prefer delayed rationality and retain disconfirmed hypotheses for further evaluation. We measured their scientific reasoning performance as well as their cognitive ability and motivational orientation. What distinguished the groups was not overall differences in these variables, but their predictive relation. For strict reasoners, better scientific reasoning was associated with faster processing, whereas for permissive reasoners, better scientific reasoning was associated with more deliberate thinking – slower processing and broader consideration of both disconfirmed and alternate hypotheses. These findings expand our understanding of “normative” scientific reasoning.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".