Bibliographic record
Abstract
Background: Although the roadblocks to development achievement in Africa emerge noticeably from resource scarcity, lack of security and good governance, or poor economic approaches, they also surface from ineffective development management practices. The monitoring and evaluation (ME) systems effectiveness assessment by the World Bank in 2007 revealed little effectiveness, mainly on cases studied in Africa.Objective: This research investigates the framework for monitoring and evaluation system effectiveness as a development management tool and shapes its measurements. It creates a framework that will help understand better the success factors of an effective ME System and how they contribute to improved development management.Methods: A trifold approach was used, which comprises three iterations — Literature review, Case Studies, and Survey. The first revisited the most relevant literature on development management and performance monitoring systems, while the second used a qualitative study of three cases in the West Africa region. The third is a survey of a sample of practitioners and managers in West Africa, where data was analysed using correlations and regressions.Results: There are significant linkages between ‘ME-System Quality’, ‘ME-Information Quality’, and ‘ME-Service Quality’. The results highlighted that the ‘Results-Based Management Practice’ of organisations, the effective ‘Knowledge and Information Management Culture’, including learning, and the ‘Evidence-Based Decision-Making Practice’ are directly influenced by effective ME System.Conclusions: Effective ME System contributes greatly to expand ‘Improved Policy and Program Design’, ‘Improved Operational Decisions’, ‘Improved Tactical and Strategic Decisions’, and ‘Improved Capability to Advance Development Objectives’.
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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 | 0.271 | 0.488 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".