Follow the leader: On the relationship between leadership and scholarly impact in international collaborations
Bibliographic record
Abstract
National contributions to science are influenced by a number of factors, including economic capacity, national scientific priorities, science policy, and institutional settings and cultures. Nations do not have equal opportunities to access the global scientific market, and therefore, often seek out international partners with complementary resources and expertise. This study aims at investigating national collaboration strategies, with a focus on research leadership-measured through corresponding authorship-and its relationship with scientific impact. Results show that countries with higher R&D investments are more scientifically independent, and confirm that international collaboration is positively related to citation impact. However, leadership in international collaboration is inversely related with a countries' share of international collaboration and there is a very little relationship between citation impact and international leadership. For instance, most countries-and particularly those that have fewer resources-have higher scientific impact when they are not leading. This suggests that, despite increasing global participation in science, most international collaborations are asymmetrical, and that the research system remains structured around a few dominate nations.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".