Project Management for Development in Africa: Why Projects are Failing and What Can be Done about It
Bibliographic record
Abstract
This article discusses international development (ID) projects and project management problems within ID in Africa and suggests they may fall into one or more of four main traps: the one-size-fits-all technical trap, the accountability-for-results trap, the lack-of-project-management-capacity trap, and the cultural trap. It then proposes an agenda for action to help ID move away from the prevailing one-size-fits-all project management approach; to refocus project management for ID on managing objectives for long-term development results; to increase aid agencies' supervision efforts notably in failing countries; and to tailor project management to African cultures. Finally, this article suggests an agenda for research, presenting a number of ways in which project management literature could support design and implementation of ID projects in Africa.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".