A bibliometric review of coach leadership studies
Bibliographic record
Abstract
This study examined published articles concerning sports leadership within the sport psychology domain over the last 30 years using bibliometric analysis that centered on the written content of the publications as unit of analysis in order to explore the intellectual base, particularly the structural relationships among relevant research components about coach leadership. Leximancer version 5.0 (Leximancer Pty Ltd.) was used to extract data from 100 sports leadership-related articles from four sport psychology journals. Overall, the most relevant concepts generated were coaches (100%) and athletes (59%), followed by study, sport, support, and motivation, and behaviors. Also, relevant concepts produced for each journal were quite similar which included coaches, athletes, behaviors, study, support and team. Further, publications related to coach leadership have shown a steady growth rate since 1990 with 76% of all published articles were conducted via quantitative research method. Finally, United States, Canada, the United Kingdom, and Belgium were the top countries involved in the area of coach leadership. Coach leadership studies generally focus on behaviors and perceptions related to the coach and relationships between leadership and psychological outcomes. Each journal has a similar but distinct rationale when publishing papers about coach leadership. Bibliometric analysis can be applied as an alternative methodology to summarize large volumes of relevant data in order to map the current knowledge as well as identify potential future research directions.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.149 | 0.161 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".