Evaluation of education and training in water and sanitation technology: case studies in Nepal and Peru
Bibliographic record
Abstract
A significant constraint to effective and sustainable water and sanitation provision is the “lack of\ncapacity at the local level” (WHO, 2010), however there is uncertainty in how the efforts of capacity\nbuilders should be measured, and improved (Brown, et al., 2001). The Centre for Affordable Water and\nSanitation Technology (CAWST) and the Institute of Non-profit Studies at Mount Royal University\n(MRU) has collaborated to address this issue. An evaluative framework, based on the Kirkpatrick model\n(Kirkpatrick, D.L. & Kirkpatrick, J.D., 2006) was developed to assist capacity builders in the water and\nsanitation sector to capture and interpret the results of their education and training activities. The\nframework was applied to evaluate CAWST’s training activities in Peru and Nepal. The findings provide\nnew perspectives on the impacts of CAWST’s work, and provide insight into how the framework can be\nvaluable to other capacity building organizations.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".