RESEARCH COLLABORATION WITHIN A DEPARTMENT OF PEDIATRICS: A SOCIAL NETWORK ANALYSIS OF COAUTHORSHIP PATTERNS
Bibliographic record
Abstract
Abstract BACKGROUND Research is a collaborative undertaking. Through collaborators, researchers have access to expertise, experience, and resources which may result in increased research productivity. Social network analysis is a set of techniques that focuses primarily on the patterns and characteristics of relationships among individuals. OBJECTIVES To describe the social network structure —the extent and patterns of collaboration among members — of a department of paediatrics and identify prominent individuals and divisions. DESIGN/METHODS We conducted a social network analysis of coauthorship. We included faculty members in a single department of paediatrics with at least 1 publication. We excluded those with a clinical appointment. We used PubMed to identify publications and Web of Science to obtain the total citations for each publication. RESULTS We included 99 faculty who authored 3 939 publications. The median (Q1, Q3) number of publications per faculty member was 12 (5, 39), ranging from 1 to 478. 83 (80%) of the faculty have coauthored a publication with another faculty member; the median (Q1, Q3) number of collaborators per faculty member was 3 (2, 8) and ranged from 0 to 21. 450 (11%) of publications included more than one faculty member as a coauthor. In the network diagram, 80 (81%) of faculty members were connected by coauthorship to a single large cluster. Neither the number of publications (increase in odds 1.0, 95% CI 1.0–1.1; p = 0.16) or h-index (increase in odds 1.0, 95% CI 1.0-1.0; p = 0.74) was associated with increased odds of a faculty member collaborating with another faculty member. Factors associated with increased odds of any two faculty members collaborating were: being from the same division (increase in odds 5.0, 95% CI 3.9–6.3; p<0.001) and both coauthoring a publication with a common faculty member (increase in odds 4.8, 95% CI 3.8–6.2; p<0.001). Being of different genders or differences in number of publications or h-index was not associated with changes in the odds of collaboration. CONCLUSION Social network analysis of coauthorship can provide insight into the social structure and research collaboration of an academic department. This structure should be considered in efforts to improve collaboration and research productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".