Behind the Scenes at a Climate Change Knowledge Sharing Network: IDS Insights from Phase One of AfricaAdapt
Bibliographic record
Abstract
Summary Knowledge sharing networks are increasingly recognised as means of mobilising the knowledge and capacities needed to respond to complex and changing realities, such as the challenges posed by climate change. AfricaAdapt is one such network that describes its aim as ‘facilitating the flow of climate change adaptation knowledge for sustainable livelihoods between researchers, policy makers, civil society organisations and communities who are vulnerable to climate variability and change across the [African] continent’. This paper takes a ‘behind the scenes’ look at the AfricaAdapt Network and the partnerships on which it is based and is thus intended to be useful for others seeking to collaboratively develop knowledge sharing networks. We focus on the dynamics of design and implementation of a knowledge sharing network in a distributed partnership, from the perspective of the former lead partner. Rather than looking at the delivery and outcomes of network activities, we explore the way in which the partners sought to develop sustainable relationships and ways of working to underpin the network, areas that are frequently under‐examined, particularly among practitioners. Areas covered include: governance and management, staffing and planning, financial management, partnership dynamics, learning, capacity development, monitoring and evaluation. Although all knowledge sharing networks are different we have tried to identify insights and principles from this specific example that can be adapted and applied in other contexts. We hope that these insights will provide a useful contribution to the broader body of theory and experience around networks and knowledge sharing.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".