Scientific production on Para Athletics: A bibliometric review
Bibliographic record
Abstract
Background: A comprehensive evaluation of the scientific production in the sport of Para Athletics, encompassing both quantity and quality, is lacking. Aim: To perform a quantitative and qualitative bibliometric analysis using science mapping techniques, to investigate publication trends, author network clusters, and keyword occurrence linked to the scientific production in the PA sport. Methods: Studies related to PA were identified throughout a search across the databases PubMed, Scopus, and Web of Science (Core Collection). Results: From an initial pool of 2,350 documents, 149 were deemed eligible and incorporated into the study. Most studies were published in the last 10 years, mainly in journals related to the fields of Sport Sciences and Rehabilitation. The keyword analysis revealed 364 unique keywords, 8 distinct clusters, 287 connections. Bibliometric examination unveiled 21 authorship network clusters, comprising 260 links. Countries that contributed with most research (75.7% of all published articles) includes United States, Canada, Brazil, United Kingdom, Germany, Netherlands, Spain, South Africa, Japan, and Australia. Conclusion: Research related to PA has increased exponentially in recent years, particularly in the fields of Sport Sciences and Rehabilitation. Collaborative research (authorship network clusters) seems prevalent, and all continents demonstrated participation in published studies, albeit with different relative contributions.
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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 | 0.034 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.190 | 0.200 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| 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".