Football coaches’ development in Brazil: a focus on the content of learning
Bibliographic record
Abstract
AIM The aim of the study was to analyze the lifelong content of learning of coaches. METHODS Eight coaches inserted in an Elite Football Club participated. Rappaport Time Line and semi-structured interviews were used to obtain the data. The coaches’ learning was organized according to the theory of Lifelong Learning.1-4 RESULTS The coaches presented in their personal experiences, with their families and as athletes, content of learning such as “leadership development” and “formation of values”. In professional experiences, such as in academic training, coach assistants and even coaching, they are also reported as essential in obtaining content of learning (general and specific knowledge, training methods, leadership development, self-control). Finally, the reflexive process is considered by most coaches as a potentiator of learning, with interference on the “coach-athlete relationship”, “activity adjustment,” among other content of learning. CONCLUSION The content learned throughout the life were defined in certain episodes for presenting different meanings in the life of the coaches, in which they related to a new experience according to their biographies. Therefore, the various episodes offer coaches new experiences, in which they can incorporate, reinforce or renew the content about the coaching process and are responsible for the development of the coach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".