What we have learnt (so far) about deliberative dialogue for evidence-based policymaking in West Africa
Bibliographic record
Abstract
Policy decisions do not always take into account research results, and there is still little research being conducted on interventions that promote their use, particularly in Africa. To promote the use of research evidence in Africa, deliberative dialogue workshops are increasingly recommended as a means to establish evidence-informed dialogue among multiple stakeholders engaged in policy decision-making. In this paper, we reflect on our experiences of conducting national workshops in six African countries, and we propose operational recommendations for those wishing to organise deliberative dialogue. Our reflective and cross-sectional analysis of six national deliberative dialogue workshops in which we participated shows there are many specific challenges that should be taken into account when organising such encounters. In conclusion, we offer operational recommendations, drawn from our experience, to guide the preparation and conduct of deliberative workshops.
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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 | 0.111 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| 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".