Recess, Cognitive Performance, and School Adjustment 2 The Role of Recess in Children’s Cognitive Performance and School Adjustment
Bibliographic record
Abstract
There can be little doubt that there has been increased emphasis on accountability in both preschool and primary school education over the past 20 years. Opinions regarding the value of this position, however, are certainly diverse. Advocates of the accountability movement, rightfully suggest that scarce tax dollars should be spent only on programs that “work.” Politicians as different as former President Bill Clinton and President George W. Bush have supported variants of this view. One of the specific, though less noticed, impacts of accountability has been that children’s opportunities for free time (in the form of recess) and corresponding opportunities to interact with their peers has been eliminated or diminished in many school systems across this country, Canada, and the UK (The Economist, 2001; Pellegrini, 2005). The popularity of the movement to minimize recess in schools may be due to the fact that politicians and school superintendents see this as a way in which to “get tough on education”, provide more “academic time ” for students, and to improve academic performance. Indeed, it may seem commonsensical to many that reducing recess time has a positive effect on achievement. After all, more time spent in academics should directly translate into improved performance (Brophy & Good, 1974).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".