The Importance of Transformational Leadership in the Quest for Group Cohesion: The Case of a University Level Varsity Football Program
Bibliographic record
Abstract
The purpose of this study was to systematically analyze and describe the behaviors (coach athlete interactions) of university level football coaches, to compare these findings to other studies and to analyze these results in light of transformational leadership. The Arizona State University Observation Instrument (ASUOI), a systematic observation instrument consisting of 14 behavior categories, was used to compile data on the interactions of seven members of the coaching staff. Event recording was used to collect the data of each coach being observed during practice sessions, these sessions were videotaped to determine the interactions between the athletes and the coaches. Segments of the practices were classified as warm-up, group and team. Analysis of the data revealed that the warm-up segment differed significantly from the group and team segments. Instruction was the behavior with the highest percentage in both the group and team segments of practice. Looking at the entire practice, instruction was also the highest occurring behavior it had the greatest percentage and rpm than any other behavior category. Praise and hustle were also two of the highest occurring behaviors. There was a strong correlation between this study and other studies using the ASUOI and the Coaching Behavior Recording Form (CBRF) in regards to instruction being the highest occurring behavior category. It was concluded that a systematic effort was made by members of the coaching staff to adhere to transformational leadership strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".