Mutual reinforcement: combining project outputs with capacity development outcomes for service delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Capacity development of permanent local institutions is needed to improve the sustainability of investments made in the water, sanitation and hygiene (WASH) sector. To check capacity development intentions, development partners (DPs) can ask the question “What capacities are you developing and why?” This will verify that capacity development is being done with precise objectives, and is aligned with institutional needs and role definitions. DPs can use implementation and capacity development objectives as mutually reinforcing opportunities to support strong project outputs as well as to improve outcomes for service delivery. Two particular techniques for capitalizing on this duality are highlighted: supporting implementers, and supporting reflective learning. Examples of practical combinations of capacity development approaches are presented from the perspective of Engineers Without Borders Canada working in collaboration with other DPs and with district governments in Malawi’s WASH sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it