The contribution of adolescent effortful control to early adult educational attainment.
Bibliographic record
Abstract
Effortful control has been proposed as a set of neurocognitive competencies that is relevant to self-regulation and educational attainment (Posner & Rothbart, 2007). This study tested the hypothesis that a multiagent report of adolescents' effortful control (age 17) would be predictive of academic persistence and educational attainment (age 23-25), after controlling for other established predictors (family factors, problem behavior, grade point average, and substance use). Participants were 997 students recruited in 6th grade from 3 urban public middle schools (53% males; 42.4% European American; 29.2% African American). Consistent with the hypothesis, the unique association of effortful control with future educational attainment was comparable in strength to that of parental education and students' past grade point average, suggesting that effortful control contributes to this outcome above and beyond well-established predictors. Path coefficients were equivalent across gender and ethnicity (European Americans and African Americans). Effortful control appears to be a core feature of the self-regulatory competencies associated with achievement of educational success in early adulthood. These findings suggest that the promotion of self-regulation in general and effortful control in particular may be an important focus not only for resilience to stress and avoidance of problem behavior, but also for growth in academic competence.
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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 | 0.001 | 0.007 |
| 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) | 0.001 | 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".