Bibliographic record
Abstract
Executive functions (EFs; e.g., reasoning, working memory, and self-control) can be improved. Good news indeed, since EFs are critical for school and job success and for mental and physical health. Various activities appear to improve children's EFs. The best evidence exists for computer-based training, traditional martial arts, and two school curricula. Weaker evidence, though strong enough to pass peer review, exists for aerobics, yoga, mindfulness, and other school curricula. Here I address what can be learned from the research thus far, including that EFs need to be progressively challenged as children improve and that repeated practice is key. Children devote time and effort to activities they love; therefore, EF interventions might use children's motivation to advantage. Focusing narrowly on EFs or aerobic activity alone appears not to be as efficacious in improving EFs as also addressing children's emotional, social, and character development (as do martial arts, yoga, and curricula shown to improve EFs). Children with poorer EFs benefit more from training; hence, training might provide them an opportunity to "catch up" with their peers and not be left behind. Remaining questions include how long benefits of EF training last and who benefits most from which activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".