Functions of observational learning in coaches and officials: New themes
Bibliographic record
Abstract
The Sport Imagery Questionnaire was used as a framework for the development of the Functions of Observational Learning Questionnaire (FOLQ). Some researchers have challenged whether the FOLQ fully captures all uses of OL, given distinctive qualities exist between OL and imagery. We examined this possibility and worked with existing data from Hancock et al. (2011) in which they extended the use of the FOLQ to coaches and officials of team interactive sports. In that research, the following open-ended question Do you observe others/self for anything not addressed above? had been included on the questionnaire but was not analyzed. Of the 210 questionnaires completed, 18 coaches and 23 officials responded to the open-ended question. The first and last authors coded participants' responses, achieving researcher consensus. Following this, the second and third authors assumed the role of critical friends. Results highlighted many responses that were grounded in the FOLQ; specifically, 72% and 69% for coaches and officials respectively. A number of responses, however, fell outside of the FOLQ. One particular theme was the use of OL to improve communication (e.g., how to talk to players) among coaches and officials. A second theme, unique to referees, was that of self-presentation (e.g., appropriate attire and conduct). Although less robust, the notion of self-reflection also emerged with the coaches. With new themes emerging, it suggests that the current FOLQ is lacking in its content structure and further research may be needed to improve the FOLQ.
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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 | 0.078 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| 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".