Exploration is associated with socioeconomic disparities in learning and academic achievement in adolescence
Bibliographic record
Abstract
Adolescents from lower socioeconomic status backgrounds often underperform on tests of learning and academic achievement. Existing theories propose that these disparities reflect not only external constraints, like limited resources, but also internal decision strategies that adapt to the early environment and influence learning. These theories predict that adolescents from lower socioeconomic status backgrounds explore less and exploit more, which, in turn, reduces learning and academic achievement. Here, we test this possibility and show that lower socioeconomic status in adolescence is associated with less exploration on a reward learning task (n = 124, 12-14-year-olds from the United States). Computational modeling revealed that reduced exploration was related to higher loss aversion. Reduced exploration also mediated socioeconomic differences in task performance, school grades, and, in a lower-socioeconomic status subsample, academic skills. These findings raise the possibility that learning disparities across socioeconomic status relate not only to external constraints but also to internal decision strategies and provide some mechanistic insight into the academic achievement gap.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".