Understanding super‐partnerships in scientific collaboration: Evidence from the field of economics
Bibliographic record
Abstract
Abstract Super‐partnerships exist between scholars connected within densely‐knit collaboration networks. Understanding how such relationships affect scholars' careers is of great importance. In this paper, focusing on the longitudinal aspects of scientific collaboration, we analyze collaboration profiles from the egocentric perspective and use analytic extreme value thresholds to identify super‐partners. A total of 5722 pairs of super‐partners are found in the field of economics. The several interesting findings about super‐partners are summarized as follows. (1) The collaboration pattern of super‐partners can be divided into three types: the dual‐core, bridge, and triangle types. (2) Gender disparities are reflected in the collaboration among super‐partners, and the stability of super‐partnerships involving different combinations of genders displays different characteristics. The random‐effect model is constructed to explore the effect of a super‐partnership on both parties from the aspects of productivity and influence, which also shows gender disparities. (3) A super‐partnership contributes to above‐average productivity and citation impacts of the publication for three collaboration patterns, and the research improvement of the triangle type is the greatest among the three types. Overall, this paper explores the characteristics of super‐partners and the added value of a long‐term commitment, which provides quantitative insights into the effect on scientific collaboration associated with close collaboration.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".