Exploring the structure of relative age effects research using citation network analysis
Bibliographic record
Abstract
Since the 1980s, research on relative age effects (RAEs) consistently shows that relatively older individuals are advantaged in sport and other contexts. With the recent proliferation of studies on RAEs, periodic knowledge synthesis becomes imperative. Our purpose was to conduct a cross-disciplinary citation network analysis of RAEs literature to enhance our knowledge of RAEs citation structures and the interconnectivity of RAEs studies. We analysed 484 RAEs articles found in Web of Science that were published before 2022. Descriptive results revealed a 12.6% annual growth rate for total RAEs articles published since 1980. The articles appeared in 151 journals, had 1,180 unique authors, and averaged 23.9 citations received. Three theoretical/review papers had the most substantial influence on the field. For the conceptual structure of the field, it was apparent that RAEs research focused mainly on sport performance, maturity, and competition. Regarding intellectual structure, three distinct clusters of articles were cited together, and 13 authorship clusters were detected with few between-cluster connections. The results describe a field with productivity but little interconnectivity among authors and papers. We offer insights into this trend and the role that influential authors/articles have in the field.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.049 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".