Implementing a Capacity Development Initiative to Build Resilience to Better Adapt to Climate Change: A Case Study in Ethiopia, Africa
Bibliographic record
Abstract
Abstract— A five-year capacity development initiative called Small-scale and Micro Irrigation Support (SMIS) Project has been funded and launched in Ethiopia by the governments of the Netherlands and Canada in close collaboration with the local government in the year 2014. The project has been mobilized to expand the capacity of agriculture and water sectors that will use the newly-provided technologies to increase yields and quality of their agricultural products as well as to strengthen their resilience to better adapt to climate change at four Ethiopian states including the state of Tigray. The capacity building plan in the Tigray state has been implemented in eight-pilot woredas (villages) and twelve running pilot irrigation schemes. To bridge the identified capacity gaps, many regional and woreda level agriculture and water sector staff as well as the farmers have been trained using the SMIS Project six-stage capacity development strategy. The progression of several related key indicators was continuously traced using the performance measurement framework (PMF) method and the results were communicated to the stakeholders utilizing results-based management (RBM) approach. The intermittent outcomes have shown that the implementation of SMIS Project capacity development initiative has created landmark changes and outstanding qualities among the relevant institutions, staff, and farmers in the pilot schemes of all sub-regions in the Tigray state. The project has promoted more efficient institutions and was able to train many skillful farmers to build resilience to better adapt to climate change when it strikes. This paper will discuss and present the project outline and its partial achievements until the project midterm.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".