The Impact of a Researcher’s Structural Position on Scientific Performance: An Empirical Analysis
Bibliographic record
Abstract
This article discusses the nature and structure of scientific collaboration as well as the association between academic collaboration networks and scientific productivity. Based on empirical data gathered from the CVs of 73 researchers affiliated with an academic research network in Canada, this study used social network analysis (SNA) to examine the association between researchers' structural position in the network and their scientific performance. With reference to Granovetter's and Burt's theories on weak ties and structural holes, we argue it is the bridging position a researcher holds in a scientific network that matters most to improve scientific performance. The results of correlation scores between network centrality and two different indicators of scientific performance indicate there is a robust association between researchers' structural position in collaboration networks and their scientific performance. We believe this finding, and the method we have developed, could have implications for the way research networks are managed and researchers are supported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.033 | 0.161 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".