The Relationships among Skill Level, Age, and Golfers’ Observational Learning Use
Bibliographic record
Abstract
The purpose of this study was to examine the influence of skill level and age on golfers’ (n = 188) use of observational learning for skill, strategy, and performance functions, as assessed by the Functions of Observational Learning Questionnaire. Golf handicap was used as an objective measure of golf skill level, with a lower handicap reflecting a higher skill level. It was hypothesized that both age and skill level would predict observational learning use, with younger and less experienced golfers reporting increased use of all three functions of observational learning. It was also predicted that age and skill level would interact to predict use of the performance function, with younger golfers employing more of that function than older golfers at the same skill level. Partial support was obtained for these hypotheses. Regression analyses revealed that the interaction of age and skill level predicted use of the skill function. Younger golfers employed more of the skill function than older golfers; however this discrepancy increased as skill level decreased. Age, and not skill level, was a significant predictor of golfers’ use of both the strategy and performance functions, with younger golfers employing more of these functions than older golfers. These results suggest that age-related factors may have a greater impact than skill-related factors on observational learning use across the lifespan.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".