Young Children Can Be Taught Basic Natural Selection Using a Picture-Storybook Intervention
Bibliographic record
Abstract
Adaptation by natural selection is a core mechanism of evolution. It is also one of the most widely misunderstood scientific processes. Misconceptions are rooted in cognitive biases found in preschoolers, yet concerns about complexity mean that adaptation by natural selection is generally not comprehensively taught until adolescence. This is long after untutored theoretical misunderstandings are likely to have become entrenched. In a novel approach, we explored 5- to 8-year-olds' capacities to learn a basic but theoretically coherent mechanistic explanation of adaptation through a custom storybook intervention. Experiment 1 showed that children understood the population-based logic of natural selection and also generalized it. Furthermore, learning endured 3 months later. Experiment 2 replicated these results and showed that children understood and applied an even more nuanced mechanistic causal explanation. The findings demonstrate that, contrary to conventional educational wisdom, basic natural selection is teachable in early childhood. Theory-driven interventions using picture storybooks with rich explanatory structure are beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".