Evaluating transdisciplinary research practices: insights from social network analysis
Bibliographic record
Abstract
Abstract Transdisciplinary researchers collaborate with diverse partners outside of academia to tackle sustainability problems. The patterns and practices of social interaction and the contextual nature of transdisciplinary research result in different performance expectations than traditional, curiosity-driven research. Documenting patterns of interaction can inform project success and affirm progress toward interim outcomes on the way to achieve sustainability impacts. Yet providing credible and robust indicators of research activity remains challenging. We provide quantitative and qualitative indicators for assessing transdisciplinary practices and patterns through social network analysis (SNA). Our assessment developed four criteria to reveal how SNA metrics provide insight into (1) diversity of participants; (2) whether and how integration and collaboration are occurring, (3) the relative degrees of network stability and fragility, and (4) how the network is structured to achieve its goals. These four key criteria can be used to help identify patterns of research activity and determine whether interim progress is occurring.
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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 | 0.030 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".