Architectures of adaptive integration in large collaborative projects
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
Collaborations to address complex societal problems associated with managing human-natural systems often require large teams comprised of scientists from multiple disciplines. For many such problems, large-scale, transdisciplinary projects whose members include scientists, stakeholders, and other professionals are necessary. The success of very large, transdisciplinary projects can be facilitated by attending to the diversity of types of collaboration that inevitably occur within them. As projects progress and evolve, the resulting dynamic collaborative heterogeneity within them constitutes architectures of adaptive integration (AAI). Management that acknowledges this dynamic and fosters and promotes awareness of it within a project can better facilitate the creativity and innovation required to address problems from a systems perspective. In successful large projects, AAI (1) functionally meets objectives and goals, (2) uses disciplinary expertise and concurrently bridges many disciplines, (3) has mechanisms to enable connection, (4) delineates boundaries to keep focus but retain flexibility, (5) continuously monitors and adapts, and (6) encourages project-wide awareness. These principles are illustrated using as case studies three large climate change and agriculture projects funded by the U.S.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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