Disposition or Disruption: How do young learners explain inconsistent causal evidence?
Bibliographic record
Abstract
The causal world is highly inconsistent. While past research demonstrates even young learners’ ability to reason from probabilistic evidence, there has been little examination of how learners reason about the nature of causal inconsistencies. For instance, what do we think about why variations occur? Four- to six-year-olds (N=90) watched either a person or machine repeatedly act in one of two possible ways. At test, the opposite behavior occurred, and children were asked whether the change was due to an internal disposition/capacity for variation or constraints in the external context. Results reveal a significant bias towards internal explanations of agents’ inconsistencies (70%, p=0.01, two-tailed binomial) and for external explanations of machines in older (63%, p=0.09, two-tailed binomial), but not younger (50%) children – suggesting young learners consider both inherent and contextual sources of variation in causal relationships and gradually develop complex expectations about the different likelihoods of these sources depending on domain.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".