Managing collaborative research: insights from a multi-consortium programme on climate adaptation across Africa and South Asia
Bibliographic record
Abstract
Abstract Collaborative research requires synergy among diverse partners, overall direction, and flexibility at multiple levels. There is a need to learn from practical experience in fostering cooperation towards research outcomes, coordinating geographically dispersed teams, and bridging distinct incentives and ways of working. This article reflects on the experience of the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA), a multi-consortium programme which sought to build resilience to regional climate change. Participants valued the consortium as a network that provided connections with distinct sources of expertise, as a means to gain experience and skills beyond the remit of their home organisation. Consortia were seen as an avenue for reaching scale both in terms of working across regions, as well as in terms of moving research into practice. CARIAA began with programme-level guidance on climate hotspots and collaboration, alongside consortium-level visions on research agenda and design. Consortia created and implemented work plans defining each organisation’s role and responsibilities and coordinated activities across numerous partners, dispersed locations, and diverse cultural settings. Nested committees provided coherence and autonomy at the programme, consortium, and activity-level. Each level had some discretion in how to deploy funding, creating multiple collaborative spaces that served to further interconnect participants. The experience of CARIAA affirms documented strategies for collaborative research, including project vision, partner compatibility, skilled managers, and multi-level planning. Collaborative research also needs an ability to revise membership and structures as needed in response to changing involvement of partners over time.
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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 | 0.000 | 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 | 0.001 |
| 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 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".