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 \n evaluation is a static exercise driven by donor reporting \n requirements. After project closure, there are seldom \n sustainable benefits of the monitoring and evaluation \n system. This paper examines how monitoring and evaluation \n can be transformed into a dynamic tool for effective project \n management, with benefits carrying over beyond the typical \n project lifecycle. The paper assesses an innovative, digital \n management information system developed under the Women \n Entrepreneurship Development Project, a Government of \n Ethiopia initiative financed by a World Bank International \n Development Association loan and grant funding from Global \n Affairs Canada. The paper examines the context of the \n development of the management information system, its \n effectiveness, and its potential for sustainability. \n Ethiopia is among the poorest countries in the world, and \n government administration units involved in administering \n projects often face funding and resource shortfalls. The \n paper demonstrates how effective and sustainable monitoring \n and evaluation systems can be developed even in challenging \n contexts such as these, by focusing on simple technical \n solutions that can be maintained and refined locally, \n ensuring low development and maintenance costs compatible \n with government monitoring and evaluation budgets, and \n linking project-level monitoring and evaluation to broader \n government operations.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".