Mapping the world of coaching science : a citation network analysis /by Sandrine Rangeon.
Bibliographic record
Abstract
The field of coaching science has experienced rapid expansion in recent years, yet it has also been criticized for its lack of conceptual clarity (Cushion, 2007; Gilbert & Trudel, 2004; Lyle, 2002). The purpose of the present study was to identify and describe key publications and influential researchers in coaching science using citation network analysis. The study sample included 141 English-language peer-reviewed coaching science research articles in 2007 and 2008. A citation network analysis was conducted on the references of the 141 articles (3,891 references) to identify the key publications influencing coaching science. These key publications were then coded for type (e.g., conceptual, empirical) and topic (e.g., efficacy, coach development). The structure of the field was revealed through the creation of a co-authorship network. Finally, a Researcher Influence Factor (RIF) was computed, taking into consideration the number of citations received by a researcher???s publications as well as his/her social capital. The citation network showed that of the top 10 publications, six were conceptual papers or books while four were empirical publications. Research on coach development was the most prominent topic among the key publications. Analysis of the most influential researchers revealed, amongst other findings, the dominance of the USA, Canada, and the UK in coaching research. Therefore, coaching science is highly influenced by a small set of key publications and researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 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".