The role of deliberate practice in chess expertise
Bibliographic record
Abstract
Abstract Two large, diverse samples of tournament‐rated chess players were asked to estimate the frequency and duration of their engagement in a variety of chess‐related activities. Variables representing accumulated time spent on serious study alone, tournament play, and formal instruction were all significant bivariate correlates of chess skill as measured by tournament performance ratings. Multivariate regression analyses revealed that among the activities measured, serious study alone was the strongest predictor of chess skill in both samples, and that a combination of various chess‐related activities accounted for about 40% of the variance in chess skill ratings. However, the relevance of tournament play and formal instruction to skill varied as a function of skill measurement time (peak vs. current) and age group (above vs. below 40 years). Chess players at the highest skill level (i.e. grandmasters) expended about 5000 hours on serious study alone during their first decade of serious chess play—nearly five times the average amount reported by intermediate‐level players. These results provide further evidence to support the argument that deliberate practice plays a critical role in the acquisition of chess expertise, and may be useful in addressing pedagogical issues concerning the optimal allocation of time to different chess learning activities. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".