Citation structures of relative age effect studies in sport
Bibliographic record
Abstract
One goal of publishing research is to share knowledge with a wide audience of interested stakeholders. For example, studies on relative age effects (RAEs) in sport can potentially have influential consequences on research and policies in sport, and also in other domains such as education, psychology, and sociology. The purpose of this study was to implement a two-phase design to understand how studies conducted on RAEs in sport are shared in sport science and across other domains. For Phase 1, we performed a citation analysis on all peer-reviewed, published articles that examined RAEs in sport (N = 86), revealing that the articles which were most cited by sport RAE researchers tended to focus on male, team sports that had significant results, while generally overlooking female, individual sports and those with non-significant results. For Phase 2, we then examined how these 86 sport articles were cited across research domains outside of sport science. Again, the articles that were cited most frequently tended to focus on male, team sports and reported the presence of a RAE. Additionally, many of the top-cited sport articles did not focus solely on RAEs; rather, they also included research topics such as birthplace, talent development, and motivation. In this paper we discuss the implications of homogeneous citation practices as well as the characteristics of research most conducive to being shared both within and between domains.Acknowledgments: This research was supported in part by a standard research grant (grant # 410-2011-0472) from the Social Sciences and Humanities Research Council of Canada.
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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 | 0.114 | 0.554 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.145 | 0.115 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".