Social network analysis as a metric for the development of an interdisciplinary, inter-organizational research team
Bibliographic record
Abstract
The development of an interdisciplinary and inter-organizational research team among eight of Canada's leading emergency, geriatric medicine and rehabilitation researchers affiliated with six academic centers has provided an opportunity to study the development of a distributed team of interdisciplinary researchers using the methods of social network theory and analysis and to consider whether these methods are useful tools in the science of team science. Using traditional network analytic methods, the team of investigators were asked to rate their relationships with one another retrospectively at one year prior to the team's first meeting and contemporaneously at two subsequent yearly intervals. Using network analytic statistics and visualizations the data collected finds an increase in network density and reciprocity of relationships together with more distributed centrality consistent with the findings of other researchers. These network development characteristics suggest that the distributed research team is developing as it should and supports the assertion that network analysis is a useful science of team science research tool.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".