Research, policy engagement and practice
Bibliographic record
Abstract
This paper examines efforts to bridge multi-disciplinary research, policy engagement and practice to improve the lives of children living in poverty in a sample of developing countries. The paper is based on the experiences of Young Lives and draws on insights from Ethiopia, India, Peru, and Vietnam. It pays particular attention to the work of the Young Lives team in Ethiopia to make children’s issues central to the Ethiopian Poverty Reduction Strategy Paper process. The paper first discusses the importance of examining and understanding the policy environment in order to increase the possibility of having a pro-child influence on policy. It then considers how Young Lives in Ethiopia has set out the key factors to ensure successful research-based advocacy. The authors stress the importance of: credible research quality, understanding of the socio-political context in which research is embedded, identifying and networking with state and civil society actors, and ensuring advocacy messages are framed in a context-appropriate way. They also present lessons learned on the timing of policy engagement; the politics of bridging research and policy; and the value of long-term partnerships between NGOs and researchers. The paper concludes by reflecting on the strengths and weaknesses of the poverty reduction strategy in Ethiopia, and outlines some general lessons for translating research into social policy change.
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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 | 0.227 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.097 |
| Scholarly communication | 0.049 | 0.037 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".