Mini managers: Children strategically divide cognitive labor among collaborators, but with a self-serving bias
Bibliographic record
Abstract
Strategic collaboration according to the law of comparative advantage involves dividing tasks based on the relative capabilities of group members. Three experiments (N = 405, primarily White and Asian, 45% female, collected 2016-2019 in Canada) examined how this strategy develops in children when dividing cognitive labor. Children divided questions about numbers between two partners. By 7 years, children allocated difficult questions to the skilled partner (Experiment 1, d = 1.42; Experiment 2, d = 0.87). However, younger children demonstrated a self-serving bias, choosing the easiest questions for themselves. Only when engaging in a third-party collaborative task did 5-year-olds assign harder questions to the more skilled individual (Experiment 3, d = 0.55). These findings demonstrate early understanding of strategic collaboration subject to a self-serving bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".