Real Versus Ideal: Understanding How Coaches Gain Knowledge
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
In an ever-evolving society, sport coaches are presented with a number of avenues through which they can acquire and refine their coaching knowledge. The purpose of this research was to replicate and extend past research to gain an up-to-date understanding of how coaches are presently gaining knowledge. This was done through a constructive replication using a sequential explanatory mixed-method design. Study 1 included 798 coaches who completed an online questionnaire detailing their use of 16 sources of coaching knowledge. Coaches’ top three most used sources were interacting with coaches, learning by doing, and observing others. In contrast, the top three most preferred sources were observing others, interacting with coaches, and having a mentor. To contextualize these findings, Study 2 used a qualitative design in which 14 coaches were interviewed to understand their experiences with different knowledge sources. Five distinct narrative types were identified: recent elite athletes, parent coaches, coach developers, teacher coaches, and experienced coaches. Coaches reported engaging in more social and unstructured learning experiences, and the reasons for their preferences appeared to differ based on lifestyle and perceived barriers. Collectively, these findings highlight how coaches gain knowledge and why they prefer certain sources over others.
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 | 0.001 | 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 | 0.001 |
| 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 it