How young children come to recommend resource choices that reduce waste
Bibliographic record
Abstract
Whereas adults are often motivated to minimize material waste, young children are notoriously wasteful of material resources. Wastefulness in children could arise because they do not see the value in minimizing material waste. We explored this possibility in four experiments on children aged 3–7 (total N = 514). Children saw vignettes where an agent chose between two resources: a smaller resource that resulted in minimal waste or a larger one that resulted in greater waste. Around 5.5 years, children indicated that others should select the smaller resource (paper and foods) when this would reduce waste, showing that they think others should minimize material waste. For example, when a person could create a paper snowflake using either a larger or smaller sheet of paper, children aged 5.5 and older recommended using the smaller sheet (reducing the amount of paper wasted as scraps). The experiments also found that this preference does not arise from a simple heuristic to choose smaller resources. Overall, our findings suggest that development in children’s responses resulted from change in their understanding of waste. However, we discuss other potential explanations for the findings, and avenues for future research. • We investigated 3–7-year-olds’ understanding of material waste. • Children judged how agents should complete goals that would produce some waste. • From age 5.5, children recommended methods of completing goals which minimized waste. • We discuss whether older children's success could reflect heuristics rather than true reasoning about waste.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".