Mapping cross-border collaboration and communication in cardiovascular research from 1992 to 2012
Bibliographic record
Abstract
AIMS: The growing burden of cardiovascular disease requires growth in research and innovation. We examine world-wide participation and citation impact across the cardiovascular research landscape from 1992 to 2012; we investigate cross-fertilization between countries and examine whether cross-border collaboration affects impact. METHODS AND RESULTS: State-of-the-art bibliometric methods and indicators are used to identify cardiovascular publications from the Web of Science, and to map trends over time in output, citation impact, and collaboration. The publication output in cardiovascular research has grown steadily from 1992 to 2012 with increased participation worldwide. China has the highest growth as relative share. The USA share initially predominated yet has reduced steadily. Over time, the EU-27 supra-national region has increased its participation above the USA, though on average it has not had greater citation impact than the USA. However, a number of European countries, as well as Australia and Canada, have improved their absolute and relative citation impact above that of the USA by 2006-2012. Europe is a hub of cross-fertilization with strengthening collaborations and strong citation links; the UK, Germany, and France remain central in this network. The USA has the highest number of strong citation links with other countries. All countries, but especially smaller, highly collaborative countries, have higher citation impact for their internationally collaborative research when compared with their domestic publications. CONCLUSION: Participation in cardiovascular research is growing but growth and impact show wide variability between countries. Cross-border collaboration is increasing, in particular within the EU, and is associated with greater citation impact.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.051 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".