Annexe: Impact Stories - Maximising the Impact of Global Development Research – A New Approach to Knowledge Brokering
Bibliographic record
Abstract
is the Communications and Impact Manager at IDS.She leads the communications for a portfolio of international development research projects and manages a team of research communications specialists.She has worked on a range of issues within the sector including health and nutrition, child labour, and gender equality with a focus on translating evidence and knowledge into accessible and engaging communications to achieve sustainable impact.As part of the Impact Initiative communications team, she has helped shape the impact storytelling process, led high-level policy events, and developed cross-synthesis policy papers.Louise Clark is the Monitoring Evaluation and Learning (MEL) Manager at IDS and has been mapping social networks for over 15 years to visualise how different groups interact and collaborate and how these relational structures facilitate knowledge exchange and support positive development outcomes.Her work involves all stages in the MEL cycle, from the strategic design of MEL frameworks and approaches, creating MEL processes and products, supporting monitoring and reporting to managing evaluations and facilitating spaces for reflection and learning.She has a particular interest in Theory of Change as a tool to support stakeholder engagement and build shared ownership of project outcomes to deliver strategies that promote behaviour change.Her facilitation supports projects to explore causal pathways and challenge assumptions about how change happens to promote reflection, learning, and improvement.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".