The development of leadership in model youth football coaches
Bibliographic record
Abstract
The purpose of this study was to examine the development of leadership among model youth football coaches. Six award-winning model youth football coaches (M age = 46.0 years, SD = 5.8), and one athlete from each stage of the coaches' careers (early, middle, recent; n = 18, M age = 24.4 years, SD = 4.3) were purposefully sampled and completed semi-structured interviews. Interviews included questions on leadership behaviours and factors that have influenced the development of the coaches' leadership. Deductive and inductive analyses were completed. First, data regarding the coaches' leadership behaviours from the coaches' and athletes' interviews were deductively coded into categories from the charismatic, ideological, and pragmatic (CIP) model of outstanding leadership (Mumford, 2006). The majority of reported behaviours the coaches used aligned with a pragmatic leadership style. However, none of the coaches' behaviours exclusively aligned with a single style. Second, data from the coaches' interviews were inductively analysed to identify factors that contributed to the development of their leadership. The following factors were identified: role models; networks of coaches; experience and reflection; and formal, non-formal, and informal learning. These factors were consistent, regardless of the coaches' leadership styles. Overall, the results of this study indicated that there may be benefit to considering broader models of leadership in coach education and in the study of leadership in sport, and establishing alternative coach education pathways for leadership development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".