Determining researcher influence in coaching science: The Researcher Influence Factor (RIF)
Bibliographic record
Abstract
How can one evaluate the influence of a particular researcher in a given field? Similar to how the impact factor measures a journal's prominence, the Researcher Influence Factor (RIF) aims to assess the prominence of individual researchers in a field of study. The RIF takes into account the citations received by a researcher's publications in a given field, as well as an estimation of a researcher's social capital through collaborations with other researchers. Specifically, the RIF formula developed and used to evaluate a researcher's prominence included the number of citations received by publications as a primary author, the number of citations received by publications as a secondary author divided by two, and the number of connections to other researchers as revealed by a co-authorship network. Analysis of the coaching science field revealed a dominance of North American researchers. Top researchers in coaching science were found to be predominantly male professors affiliated with kinesiology departments at North American universities. Overall, the field appears to be influenced by a small set of researchers with similar profiles, but specialized in different research areas of coaching science. The RIF formula will also be discussed in comparison to other ways of assessing researchers' influence such as the h-index.Acknowledgments: Funding support provided by the Division of Graduate Studies, California State University, Fresno
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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 | 0.132 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".