Bibliographic record
Abstract
This study examined how model youth football coaches developed their leadership styles. Six model youth football coaches (M age = 46 years, SD = 6), and one athlete from each stage of the coaches’ careers (early, middle, recent; n = 18, M age = 24 years, SD = 4) were purposefully sampled. Each participant completed an initial semi-structured interview. Coach interviews focused on their leadership behaviours and factors that contributed to their development as leaders. Athlete interviews focused on their former coaches’ leadership behaviours. The coaches then participated in second interviews to further explore factors that contributed to their evelopment as leaders. Data analysis was informed by Thorne’s (2016) interpretive description methodology. Results were organised in two sections. The first section presents the coaches’ and athletes’ perspectives of the coaches’ leadership styles, based on the charismatic, ideological, and pragmatic (CIP) model of outstanding leadership (Mumford, 2006). The majority of the coach behaviours as reported by coaches and athletes aligned with a pragmatic leadership style. However, none of the coaches or athletes reported behaviours that aligned entirely with one style, and each coach demonstrated some mixed leadership behaviours from each style. The second section presents factors that contributed to the development of outstanding leadership in model football coaches. Role models; networks of coaches; experience and reflection; and formal, non-formal, and informal learning were identified as factors that contributed to the development of outstanding leadership. Practical implications that arose from these findings are discussed, including the utility of teaching coaches about a range of leadership behaviours and styles, and creating mentorship and networking opportunities for coaches to develop their leadership.
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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 | 0.003 | 0.006 |
| 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 | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".