Taking Management Digital : Lessons from the Development of an Innovative Management Information System for Small Businesses in Ethiopia
Bibliographic record
Abstract
In many aid projects, monitoring and evaluation is a static exercise driven by donor reporting requirements. After project closure, there are seldom sustainable benefits of the monitoring and evaluation system. This paper examines how monitoring and evaluation can be transformed into a dynamic tool for effective project management, with benefits carrying over beyond the typical project lifecycle. The paper assesses an innovative, digital management information system developed under the Women Entrepreneurship Development Project, a Government of Ethiopia initiative financed by a World Bank International Development Association loan and grant funding from Global Affairs Canada. The paper examines the context of the development of the management information system, its effectiveness, and its potential for sustainability. Ethiopia is among the poorest countries in the world, and government administration units involved in administering projects often face funding and resource shortfalls. The paper demonstrates how effective and sustainable monitoring and evaluation systems can be developed even in challenging contexts such as these, by focusing on simple technical solutions that can be maintained and refined locally, ensuring low development and maintenance costs compatible with government monitoring and evaluation budgets, and linking project-level monitoring and evaluation to broader government operations.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".