Mobilizing Climate Information for Decision-Making in Africa: Contrasting User-Centered and Knowledge-Centered Approaches
Bibliographic record
Abstract
This study examined ways in which climate information was mobilized for use under Future Climate for Africa (FCFA), an applied research program to improve the use of climate information to support medium-term (5–40 years) policies and planning in sub-Saharan Africa. Past research has underscored the interdependent relationship between user engagement and knowledge mobilization in effective climate knowledge uptake. The study used a document analysis of 46 program ou tputs and semi-structured interviews with 13 FCFA researchers to contrast user-centered and knowledge-centered approaches to effectively mobilize climate information uptake for use. A total of 20 knowledge mobilization tools and approaches were identified across the program and analyzed. This analysis reveals a complex interplay between user engagement and knowledge mobilization processes, including the strategic or flexible use and re-use of knowledge products as the user engagement process evolved. These findings have important implications for future programmatic design and planning in promoting engagement and mobilization approaches that can contribute to long-term policy and decision-making.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".