Coaches’ and Officials’ Self-Reporting of Observational Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it