Shaping the way we learn to coach: The childhood learning experiences of five women coaches
Bibliographic record
Abstract
Research on how coaches learn to coach has explored how they learn in formal and nonformal coaching education courses, and how they learn in informal experiences on the job, including how they learn from their athletes, other coaches, and mentors (for example, Mallett, Trudel, Lyle, & Rynne, 2009; Werthner & Trudel, 2006, 2009). Jarvis (2006) offers a theory that learning is lifelong and occurs when an individual experiences a situation that is transformed, through thoughts, emotions, and/or actions, into knowledge, beliefs, attitudes, values, and skills. What a person has learned will influence how she or he experiences new learning situations. As part of a larger dissertation research study on the lives of women coaches, the purpose of this presentation is to illustrate how preconscious learning in childhood, through primary and secondary socialization, including the social environment, family life, school, and athletic experiences, contributed to five Canadian women coaches' approaches. Through four in-depth interviews with each of the participants, the learning experiences of the women were transformed into narratives. A thematic analysis was performed to delineate how preconscious and incidental learning in childhood influenced the women's coaching knowledge, beliefs, attitudes, values, and skills. This presentation serves to broaden the scope of learning to help understand influences that impact coaches' biographies, their coaching knowledge, and coaching approach. Acknowledgments: Jarvis, P. (2006). Towards a comprehensive theory of human learning: Lifelong learning and the learning society (Vol. 1). New York, NY: Routledge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".