Supporting evidence-based decision-making: Capacity Building through Research
Bibliographic record
Abstract
Abstract Lack of data inhibits informed decision-making and is a critical challenge in developing countries, many of which are under-resourced in financial, technical and institutional capacity to collect and analyse the required data. This limits the countries’ ability to achieve development goals and keeps them dependent on the provision of external resources. Development initiatives often treat capacity building and research as two separate tracks of development. While efforts have been made in the health sector to combine these through project-based learning, this approach is relatively unexplored in the water sector which by its inter-sectoral nature stands to benefit significantly from a more collaborative and solution-oriented development strategy. Capacity Building through Research (CBtR) facilitates data collection and analysis by local researchers, mentored by international experts, strengthening local capacity to produce credible evidence able to inform sustainability-related decision-making. Five case studies piloting CBtR are discussed here and evaluated through criteria of the Dutch Strategy Evaluation Protocol framework. CBtR is shown to be a long-term strategy that requires the strengthening of cross-disciplinary networks to enhance the capacity of water management institutions, which likely contributes to more efficient evidence collection and analysis suitable for decision-makers, leading to greater national resilience and reduced need for external support.
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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 | 0.686 | 0.646 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.035 | 0.030 |
| Open science | 0.011 | 0.038 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".