An Evaluation Toolkit for Small NGOs in Water‐based Development
Bibliographic record
Abstract
Abstract Small NGOs in international development have a unique advantage when it comes to engaging communities, establishing partnerships and advocating rights. However, studies suggest that small NGOs are disadvantaged when it comes to demonstrating evidence‐based impact. Small NGOs are less likely to invest in development evaluation as they lack expertise and/or feel strongly about funding programmes and not processes, given the increased demand for accountability to donors. To explore the challenge NGOs face in moving from a single project‐based entity to a vision‐led professional entity, we have documented the creation of low‐cost evaluation tools using a case study in water‐based development. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.217 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".