Coaches’ and Officials’ Self-Reporting of Observational Learning
Bibliographic record
Abstract
Sport participants continually seek methods to hone their skills and achieve expert performance. One means to achieve this is through the use of observational learning (OL). The Functions of Observational Learning Questionnaire (FOLQ) was created to measure the types of OL athletes used. The data presented herein builds from prior research in which the use of the FOLQ was extended to coaches and officials. The researchers included the following open-ended question: “Do you observe others/self for anything not addressed above?” Responses to this question, however, have yet to be reported. As such, the purpose of this study was to analyze participants’ responses to understand how coaches and officials use observational learning. Many identified codes encompassed ideas already included within the FOLQ; however, new coding categories emerged. Specifically, coaches reported using observational learning for Self-Reflection , officials reported using observational learning for Self-Presentation , and both groups reported using observational learning to improve Communication . These results demonstrate the importance of OL to coaches’ and officials’ development. Further, the results highlight that the FOLQ might overlook coaches’ and officials’ uses of OL. Regardless, the various uses of OL ought to be included in coaching and officiating education programs to foster elite performance.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".