Understanding Interdisciplinary Collaborations as Social Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The dynamics of interdisciplinary collaboration invite further investigation if we are to make this endeavour more rewarding and productive. We are using social network analysis to track the development of a new interdisciplinary collaboration on complex interventions to improve population health. It involves nineteen scholars across four countries. We report the Baseline network of formal relationships among the scholars, along with the impact of the collaboration on these relationships in the first 18 months. We observed statistically significant increases in the density of six types of relationship networks: citing publications by other members of the collaboration, email contact, meeting with each other (outside of the formal annual meeting), visiting one another's institution, submitting research grants together and working on research projects together. The initial strategic role in the network of key 'gate keepers' has not altered substantially (betweenness centralization of the networks), but reciprocity has increased, that is, people are more likely to cite those who have cited them and work together. Increased collaboration is also reflected in the rise in number of subgroups over time and the increase in the average number of subgroup memberships. Use of social network analysis to understand the dynamics of interdisciplinary collaborations is a relatively new field. It invites reflection about what the optimal network structures for interdisciplinary collaborations would look like.
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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 | 0.021 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.070 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it